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17,891 results found
  • Carnegie Mellon University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    RubyStar is a dialog system designed to create “human-like” conversation by combining different response generation strategies. RubyStar conducts a non- task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation
  • Emory University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We describe an information-oriented conversational AI system, EmersonBot, developed for the Alexa Prize 2017 competition. The main goal of the system was informing users about current events, and answering their questions, while maintaining a fluent conversation. The main innovations of Emersonbot include the development of a federated multi-source information retrieval system that is aware of the conversation
  • Czech Technical University in Prague
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition
  • University of California, Santa Cruz
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We present our modular system, highlighting our different data sources and how we use the human mind as a model for data management. Additionally we build and employ natural language understanding and information retrieval tools
  • Seoul National University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this work, we designed a conversational system by combining a finite state method, a retrieval model and a machine-initiative dialogue strategy. By using the machine-initiative strategy, most of the user queries are handled by the finite state machine which models predefined dialogues. If the user’s utterance is out of the range of modeled dialogues, the retrieval model processes the input. On comparative
  • University of Washington
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    This paper introduces the University of Washington’s Alexa Prize socialbot, Sound- ing Board, which is designed to engage users with a wide variety of content. The system models the user utterance using a multi-dimensional representation. A hierarchical dialogue manager is employed where a master manages the overall conversation and a collection of miniskills manage different conversation segments. The
  • University of Trento
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Open–domain dialogue systems should be able to cover a very large set of domains and at the same time keep the user engaged in the interaction. Current approaches to dialogue modeling are divided between domain–independent, non–modular approaches using sequence–to–sequence models and the domain–specific modular systems developed for task–based dialogue. Furthermore, user engagement in dialogue, addressed
  • Carnegie Mellon University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Recent years have seen a surge in consumer usage of spoken dialog systems, due to the popularity of voice assistants. While these systems are capable of answering factual questions or executing basic tasks, they do not yet have the capability to hold multi-turn conversations. The Alexa Prize challenge provides us a great opportunity to explore various approaches and dialog strategies for building a multi-turn
  • Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this
  • Princeton University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We present Pixie, a socialbot submitted to the 2017 Alexa Prize, developed with the objective of holding engaging casual conversations on open-ended topics. We discuss the modular, heterogeneous architecture of the Pixie system, and highlight the successes and shortcomings of our primarily template-based approach. We reflect on our findings about alternative paradigms used in the prototyping phase; in particular
  • University of California, Berkeley
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this document we outline the data collection methods, system, and experiments we ran during the course of this project. We aim to shed light on what drove the development of our bot and the techniques we used to power our bot in the course of the conversation.
  • Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In recent years, chat-bots that assist people’s daily life gained tremendous popular- ity, such as Siri, Cortana, Alexia and Google assistant. Typically, conversations with chat-bots are task orientated and brief. In this work, we explore creating social bot that can complete task and entertain user. We proposed a two-layered dialogue framework which allows for flexible reuse and reorganization of individual
  • University of Edinburgh
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training
  • University of Montreal
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We present a novel, large-scale ensemble-based system for the Amazon Alexa Prize competition. Our system leverages state-of-the-art methods from deep learning and reinforcement learning. We carry out A/B testing experiments with real-world users and demonstrate that our approach yields substantial improvements over several baseline systems. During the competition semi-finals, our best performing system
  • Daniele Ferone, Paola Festa, Antonio Napoletano, Mauricio G. C. Resende
    LION 2017
    2017
    We propose a new smart local search for the p-center problem, based on the critical vertex concept, and embed it in a GRASP framework. Experimental results attest the robustness of the proposed search procedure and confirm that for benchmark instances it converges to optimal or near/optimal solutions faster than the best known state-of-the-art local search.
  • Estimating the relevance of documents based on the user feedback is an essential component of search, retrieval and ranking problems. User click modeling in search has focused primarily on factoring out the position bias. It is easy to see that the query type (generic queries vs specific queries) and user intent (purchase vs exploration) also introduce a bias in the click signal. In other words, the results
  • Maryam Fazel-Zarandi, Shang-Wen Li, Jin Cao, Jared Casale, Peter Henderson, David Whitney, Alborz Geramifard
    NeurIPS 2017
    2017
    Modern virtual personal assistants provide a convenient interface for completing daily tasks via voice commands. An important consideration for these assistants is the ability to recover from automatic speech recognition (ASR) and natural language understanding (NLU) errors. In this paper, we focus on learning robust dialog policies to recover from these errors. To this end, we develop a user simulator
  • Brian King, I-Fan Chen, Yonatan Vaizman, Yuzong Liu, Roland Maas, Sree Hari Krishnan Parthasarathi, Björn Hoffmeister
    Interspeech 2017
    2017
    A challenge for speech recognition for voice-controlled household devices, like the Amazon Echo or Google Home, is robustness against interfering background speech. Formulated as a far-field speech recognition problem, another person or media device in proximity can produce background speech that can interfere with the device-directed speech. We expand on our previous work on device-directed speech detection
  • Ashwin Ram, Rohit Prasad, Chandra Khatri, Anushree Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents
  • VLDB 2017
    2017
    We present a platform built on large-scale, data-centric machine learning (ML) approaches, whose particular focus is demand forecasting in retail. At its core, this platform enables the training and application of probabilistic demand forecasting models, and provides convenient abstractions and support functionality for forecasting problems. The platform comprises of a complex end-to-end machine learning
US, CA, San Francisco
Join Amazon's Frontier AI & Robotics team and help shape the future of intelligent robotic systems from the inside out. As a Member of Technical Staff - Firmware Engineer, Electronics, you will develop the low-level firmware that brings our in-house robotic actuators to life—writing the embedded code that bridges sophisticated hardware and the high-level AI control systems that power our next-generation robots. Your work will directly enable our robots to see, reason, and act in real-world warehouse environments, making you a critical contributor to one of the most ambitious robotics programs in the world. Key job responsibilities • Develop, test, and optimize embedded firmware for custom in-house robotic actuators, including motor control algorithms (FOC, commutation, current/torque/speed/position loops) running on microcontrollers and DSPs • Design and implement real-time firmware for actuator state estimation, fault detection, and protection logic, ensuring robust and safe operation across all actuator variants deployed in FAR's robotic systems • Collaborate with electronics engineers and motor design engineers to define firmware requirements, hardware interfaces (SPI, I2C, CAN, EtherCAT, RS-485), and actuator bring-up procedures for new hardware revisions • Develop and maintain firmware for field-oriented control (FOC) and sensored/sensorless motor commutation, including tuning current regulators, velocity controllers, and position controllers for high-performance robots • Build and maintain firmware test frameworks and hardware-in-the-loop (HIL) test environments to validate firmware behavior across actuator operating conditions, edge cases, and failure modes • Partner with controls engineers and AI researchers to ensure firmware-level interfaces support high-bandwidth, low-latency communication required by whole-body control and motion planning algorithms • Contribute to actuator firmware architecture decisions, define software-hardware interface standards, and maintain firmware documentation and version control practices to enable scalable multi-actuator development • Support rapid hardware bring-up and debugging of new actuator prototypes, leveraging oscilloscopes, logic analyzers, and custom diagnostic tools to characterize and validate firmware behavior on novel hardware A day in the life Your day is rooted in the intersection of hardware and software where you’ll be wiring firmware from scratch to control custom motors. You might start your morning reviewing firmware behavior logs from the previous night's actuator characterization runs, then spend time working alongside motor design and electronics engineers to debug a torque ripple issue in the motor control loop. In the afternoon, you could be writing and validating embedded firmware for a new actuator variant, tuning (field-oriented control) FOC algorithms, and collaborating with the controls team to ensure firmware interfaces align with high-level motion planning requirements. Beyond the bench, you'll participate in architecture reviews with hardware and software engineers, contribute to code reviews, and document firmware specifications that enable smooth hardware handoffs. You'll be working on actuator variants—each with unique power, torque, and speed requirements—and you'll be the firmware voice in cross-functional design discussions that shape how our actuators are built and controlled. The pace is fast, the problems are novel, and the impact is direct. About the team Frontier AI & Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.
US, CA, San Francisco
Join our Frontier AI & Robotics team to support the hardware integration of next-generation robotic systems that will transform how robots perceive and interact with the world. You'll take ownership of hands-on hardware assembly, software integration, and system validation tasks across advanced actuators, precision sensors, and robotic subsystems — ensuring they work seamlessly together to support breakthrough AI research and real-world deployment. Key job responsibilities - Assembly, Integration & DFx — Assemble and integrate robotic hardware (actuators, sensors, vision systems, machined components). Execute assembly processes and test protocols developed with engineering. Provide DFM/DFA feedback and perform simple mechanical/electrical/software design tasks; support integration/debug and partner with engineers to optimize manufacturability and testability. - R&D Prototype Test & Validation — Validate hardware revisions, verify mechanical assemblies, power sequencing, communication interfaces, and peripherals during bring-up. - Debugging & Failure Analysis — Troubleshoot and root-cause issues across the robotic platform (power, compute, comms, actuators, sensors). Conduct failure analysis from component to system level. Reproduce critical failures, interpret schematics, and bridge communication between the lab and engineering teams. - Technical Documentation — Author and maintain runbooks, failure analysis reports, assembly guides, and troubleshooting guides; uphold consistent documentation standards across the lab. - Mechanical Design Support — Perform simple R&D design tasks and test fixture design in CAD, ensuring quality and alignment with engineering priorities. - Lab Operations Support — Support machine shop capabilities, equipment maintenance, inventory management, vendor coordination, and safety/regulatory compliance. - Test Capability Development — Develop test methodologies, design jigs/fixtures, support hardware-in-the-loop (HIL) testing, and streamline failure-to-resolution workflows. A day in the life Your focus centers on the hardware and software that powers our advanced robotic platforms. You'll execute high degree-of-freedom (DoF) robotic prototype assembly and validation, working alongside engineers and fellow technicians. Your responsibilities include building, debugging, validating prototype, performing critical component and assembly quality assessments, providing DFM/DFA feedback to engineers, and designing test jigs and fixtures. Throughout the day, you balance complex assemblies and integration testing while handling urgent prototyping requests, documentation updates, and preparation for upcoming milestones. You're switching between working at the bench, collaborating in design reviews with engineers, and ensuring lab safety and equipment maintenance. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join Amazon's Frontier AI & Robotics team as a Member of Technical Staff, this Technical Program Manager will become the driving force behind breakthrough robotics innovation. You'll orchestrate complex, cross-functional programs that bridge AI research, software, hardware, and production deployment—managing the technical workstreams that enable robots to see, reason, and act in Amazon's warehouse environments. Your program leadership will directly accelerate our mission to build the next generation of embodied intelligence. Key job responsibilities · Establish and drive program management mechanisms and cadence for complex robotics and AI development initiatives spanning research, software engineering, hardware, and operations · Manage end-to-end program execution across the full robotics stack—including AI models, software engineering, and hardware deployment · Drive decision-making velocity by facilitating tradeoff discussions when there are conflicting priorities; determine whether decisions are one-way or two-way doors · Own program-level risk management, proactively identifying technical, schedule, and resource risks; escalate where necessary and drive mitigation strategies · Manage dependencies and scope changes across internal teams and partner organizations, ensuring alignment on commitments, timelines, and technical requirements · Create transparency through clear RACI frameworks, program dashboards, and communication mechanisms that keep stakeholders aligned on status, risks, and decisions · Exercise strong technical judgment to influence program-level decisions on deployment methodology, scalability requirements, and technical feasibility—acting as the voice back to research and engineering teams · Build sustainable program management processes that scale as our organization grows, adapting agile frameworks to the unique challenges of AI robotics A day in the life Your focus centers on driving velocity and alignment across our robotics programs. You might start your morning facilitating tradeoff decisions between AI researchers and software engineers on a critical prototype milestone, then transition to managing dependencies across hardware and operations teams to keep timelines on track. In the afternoon, you could be conducting risk assessments on supply chain constraints that impact our development roadmap, updating program dashboards to provide leadership visibility, or working with partner teams to align on deployment strategies. You'll establish the mechanisms and cadence that keep our fast-moving organization synchronized—from sprint planning rituals to cross-functional design reviews. Throughout the day, you balance hands-on program execution with strategic escalation, ensuring technical decisions align with our long-term vision while removing obstacles that slow teams down. You're the connective tissue that enables researchers, engineers, and operations specialists to move fast together. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
We are seeking a hands-on Electrical Engineer to lead the design and integration of electrical systems or subsystems for high-degree-of-freedom robotic platforms. This role involves architecting the robot’s power distribution, sensor wiring, and embedded electrical infrastructure. You will be responsible for designing across the full electrical system for advanced robotics platforms including power distribution, sensing, compute, motor controllers, communication infrastructure, battery system and power electronics in close collaboration with mechanical, controls and software engineers. You’ll play a key role in ensuring high-performance, reliable operation of complex electromechanical systems under real-world conditions. Key job responsibilities * Electrical system architect / owner for power electronics, actuation, PCBAs, battery, ware harness specs and high speed electrical/communications protocols * Design, develop and integrate power distribution, embedded electronics, motor controllers and safety-critical circuits for complex robotic systems * Own board layout of PCBAs including SoCs, microcontrollers, sensors, power devices, etc. using Cadence OrCAD/Allegro or equivalent tools. Oversee bring-up and validation * Determine appropriate high speed electrical and communication protocols (e.g., CAN, EtherCAT, USB, etc) for reliable and efficient system operation * Specify and design custom power electronics and power distribution boards to meet performance, thermal, and safety requirements * Design and route all cabling and wire harnesses across the robotic platform, considering EMI, signal integrity, serviceability, and integration with mechanical structures * Architect and integrate the robot’s battery system, including protection circuitry, battery management, charging systems, and thermal considerations * Define and implement wiring and electrical interfaces for sensors (e.g., lidar, stereo cameras, IMUs, tactile) and compute modules * Ownership over prototyping and bringing up electrical designs and creation of test & validation rigs About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join our Frontier AI & Robotics team to lead the hardware integration of next-generation robotic systems that will transform how robots perceive and interact with the world. You'll take ownership of critical hardware components, from advanced actuators to precision sensors, ensuring they work seamlessly together to support breakthrough AI research and real-world deployment. Key job responsibilities - Prototype Lab Leadership — Lead & develop a cross-functional technician team supporting robotic prototype hardware; own daily priorities, team KPIs, and risk communication to FAR leadership. Serve as the technical escalation point for the lab. - Assembly, Integration & DFx ownership — Assemble & integrate robotic hardware (actuators, sensors, vision, machined components). Build assembly processes and test protocols with hardware engineering. Drive DFM/DFA feedback and own simple mechanical/electrical design tasks, lead integration/debug, and partner with engineers to optimize manufacturability and testability. - Own R&D prototype test & validation — Validate hardware revisions, verify mechanical assemblies, power sequencing, comms interfaces, and peripherals during bring-up. - Build a strong debugging & failure analysis function — Troubleshoot & root-cause across the full robot platform (power, compute, comms, actuators, sensors); hands-on for complex issues, directing the team on routine ones. Conduct failure analysis from component to system level using oscilloscopes, logic analyzers, and multimeters; train technicians on diagnostic techniques. Reproduce critical failures, interpret schematics, and bridge communication between the lab and engineering teams. - Own lab technical documentation — Own documentation & quality - author runbooks, FA reports, assembly guides and troubleshooting guides; mentor the team to maintain consistent standards. - Own mechanical design for the lab — Own mechanical design technician output. Oversee technicians performing simple R&D design tasks and test fixture design, ensuring quality and alignment with engineering priorities. - Manage prototyping lab operations — oversee machine shop capabilities and quality, equipment/inventory, vendor coordination, and safety/regulatory compliance. - Build additional lab capabilities — develop test methodologies, design jigs/fixtures, implement HIL testing, and streamline failure-to-resolution workflows. A day in the life Your focus centers on the hardware that powers our advanced robotic platforms. You'll lead a strong robotics technician and lab engineering team to support high degrees of freedom (DoF) robotic hardware prototype assembly and validation. Your team will be responsible for building, debugging and validating prototype hardware, critical component and assembly quality assessments, providing DFM/DFA feedback to engineers and designing test jigs and test set-ups. You’ll manage responsibilities like quality inspections of incoming parts, one-on-ones with technicians, and coordinating machine shop operations. Throughout the day, you balance leading your team through complex assemblies and integration testing while also handling urgent prototyping requests, documentation updates, and planning for upcoming milestones. You're switching between working at the bench alongside your technicians, collaborating in design reviews with engineers, and ensuring lab safety and equipment maintenance. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join Amazon's Frontier AI & Robotics team and take ownership of the electronics that make our robots move. As a Member of Technical Staff - Electronics Engineer, Actuators & Drives, you will conceptualize, design, and test the motor drive electronics that power our in-house robotic actuators—from the gate drivers and power stages that command motor current to the sensing circuits and communication interfaces that give our robots proprioceptive awareness. Your printed circuit board (PCB) designs will live inside each of our next-generation robotic systems, directly enabling the embodied intelligence that is central to FAR's mission. Key job responsibilities • Conceptualize, design, and validate motor drive electronics for in-house robotic actuators, including inverter power stages, gate driver circuits, current and position sensing, and power management subsystems from concept through prototype and production • Lead PCB-level design of compact, high-power-density motor drive boards, including schematic capture, component selection, and collaboration with PCB layout engineers to achieve signal integrity, thermal, and EMC requirements in constrained actuator form factors • Characterize and optimize inverter switching performance, efficiency, and thermal behavior across the full operating envelope of FAR's actuator variants, using bench measurements and simulation to guide design decisions • Define and implement current sensing architectures (shunt-based, Hall-effect, or integrated IC-based) and position/velocity sensing interfaces (encoder, resolver, Hall sensor) to support high-bandwidth FOC firmware on microcontrollers and DSPs • Partner with firmware engineers to define hardware-software interfaces for motor drive control loops, fault detection logic, and communication protocols (CAN, EtherCAT, SPI), ensuring electronics designs support the real-time control requirements of robotic actuation • Collaborate with motor design and mechanical engineers to specify the electrical characteristics of custom BLDC and PMSM motors, align inverter design to motor parameters, and validate the integrated actuator electro-mechanical system • Lead hardware bring-up, functional testing, and failure analysis for new actuator electronics prototypes, developing test plans and characterization setups that systematically validate design performance and identify failure modes • Define electronics design standards, review processes, and design-for-manufacturability (DFM) guidelines for FAR's actuator drive portfolio, and mentor junior engineers in motor drive electronics design best practices A day in the life Your day centers on the full electronics development cycle for our custom actuator drive systems. You might start by reviewing simulation results for a new inverter topology, then transition to the lab to characterize switching losses and thermal performance on a prototype motor drive board. Later in the day, you could be collaborating with motor design engineers on back-EMF waveform analysis, refining gate drive timing to optimize inverter efficiency, or working with firmware engineers to define current sensing interfaces and hardware abstraction layers. Across the week, you'll be involved in schematic capture and PCB layout reviews with your design team, participating in design review gates, and iterating on hardware based on test findings. You'll navigate the challenge of fitting high-performance drive electronics into compact, thermally constrained actuator packages—designing for the power density, reliability, and robustness our robots demand. Your work will span from concept and architecture through silicon bring-up, and you'll play a key role in defining the electronics roadmap for FAR's actuator portfolio. About the team Frontier AI & Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.
US, CA, San Francisco
About the Role: We are looking for a Member of Technical Staff - Mechanical Engineer with a passion for building complex robotic systems from the ground up. This role is ideal for someone with a deep understanding of structural and electromechanical design, who thrives in hands-on environments and has experience taking high-performance robots from concept to production. You will work on the mechanical and system architecture of advanced robotics platforms, including high degree-of-freedom systems, where considerations such as actuator selection, thermal constraints, cabling, sensing integration, and manufacturability are critical. This is a cross-disciplinary role requiring close collaboration with electrical, software, and AI research teams. Beyond day-to-day hardware development, this role also provides exciting avenues to contribute to innovative research projects. Whether you’re interested in mechatronics, sensor integration, or novel actuation methods, you’ll find opportunities to explore your research interests while building real-world systems that advance in the field of high degree-of-freedom robotics. What You Bring: * A systems-thinking mindset with a strong grasp of cross-domain engineering tradeoffs. * A bias toward action: comfortable building, testing, and iterating rapidly. * A collaborative and communicative working style — especially in multi-disciplinary research environments. * A passion for robotics and advancing the state of the art in intelligent, capable machines. Key job responsibilities * Lead mechanical design of robotic subsystems and full platforms, including structures, joints, enclosures, and mechanisms for a research environment. * Own kinematic, dynamic, and structural analyses to guide the design and optimization of full systems and subsystems of high-DoF robots * Specify and integrate actuators and motors for high-torque density applications in high-degree-of-freedom systems. * Contribute to thermal management strategies for motors, sensors, and embedded compute hardware. * Integrate sensors such as lidar, stereo cameras, IMUs, tactile sensors, and compute modules into compact, functional assemblies. * Design and route cabling and wire harnesses, ensuring reliability, serviceability, and thermal/electrical integrity. * Prototype and test mechanical systems; support hands-on builds, debug sessions, and field testing. * Conduct root cause analysis on system-level failures or performance issues and implement design improvements. * Apply Design for Manufacturing (DFM) and Design for Assembly (DFA) principles to transition prototypes into scalable builds (10s–100s of units). * Collaborate with cross-functional teams in electrical engineering, controls, perception, and research to meet research and product goals. About the team Frontier AI & Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
US, CA, San Francisco
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.