pixie.jpg
Location: Princeton, NJ, USA
Faculty advisor: Sanjeev Arora

Pixie

We're an eclectic team of research-oriented undergraduate and graduate students in Princeton's CS and math departments.

Individually, our specialties span a wide gamut, from machine learning theory to computer vision to distributed systems. We're united by a passion for the multifaceted field of artificial intelligence, and a vision of bringing change and surprise to the world through our research. Using a combination of tried-and- true techniques in natural language processing and freshly minted methods in deep learning, we hope to bring to you a socialbot that will understand and react to the social context, providing endless interesting and empathetic conversation.

Niranjani P. - Team leader

I'm a second year PhD student in Computer Science, advised by Professor Barbara Engelhardt. In 2013, I graduated from the University of Cambridge in Information and Computer Engineering (BA, MEng). Following that, I was with a start-up for two years, working on the research and development of speech recognition software. My current research interests are primarily in machine learning methods motivated by clinical medicine, spanning reinforcement learning, time series modelling, natural language processing and knowledge representation.

Alex B.

I'm a second year CS PhD student advised by Han Liu working on statistical learning and deep learning. At Princeton, I've worked on robustness of machine learners to attack (paper accepted at NIPS) and online hyperparameter optimization for deep networks. I also did a research internship at Google working on transfer learning for speech recognition with deep recurrent networks. Before Princeton I worked at Wynyard on stochastic process models of crime and distributed network security software for Apache Spark. My undergraduate research was on signal processing algorithms for ventilator management in the intensive care unit.

Ari S.

I'm a second year Computer Science PhD student working with Han Liu. I am interested in both general machine learning methodologies and applications in computer vision, robotics, and natural language processing. I am supported by an NDSEG Fellowship. Before Princeton I completed a research fellowship at the National Institutes of Health, focusing on computer-aided diagnostics. I developed software for automated detection of pathologies (e.g., enlarged lymph nodes, tumors) on CT and MRI images. Prior to NIH, I studied mathematics as an undergraduate at the University of Florida.

Cyril Z.

I'm a PhD student in Computer Science, studying algorithms and machine learning theory. I received my B.S. in Computer Science from Yale University, where I worked on fast Laplacian solvers, exoplanet physics, and various artsy things. I dream of uniting the beauty and rigor of theoretical computer science with the humanism and pragmatism of its applications.

Daniel S.

Daniel is a second-year graduate student working at the intersection of artificial intelligence and distributed systems. After receiving his bachelor's in Computer Science from Harvard, he spent five years in industry working on three-dimensional computer vision, constructing laser scanners with high dynamic range, and cluster computing on three-dimensional data. In the last year, he has built a robot that autonomously scans large indoor spaces in real time powered with a distributed computing back end. He was also on the MIT-Princeton team that took 3rd place at the 2016 Amazon Picking Challenge (top non-industry entrant). He currently works on deadline computing.

Davit B.

I graduated UCL majoring in Computer Science supervised by Prof. Lourdes Agapito. I developed Cyclop War during New Year's night. Launched multi-platform casual game Froo Zoo played by 100K users at age 17. At 18 I was featured by TechCrunch and started Newsly. At 19 I founded Cyclop. I am inspired by Elon Musk, Steve Jobs, DeepMind and the possible applications of Recurrent Neural Networks in vision. I am also co-founder Castly.tv, which is a video on demand platform that lets users sync-watch movies with friends and family. Started my PhD at 20.

Holden L.

I am a third-year PhD student advised by Sanjeev Arora. My research is on provable algorithms for machine learning, including areas such as neural networks, natural language processing, and reinforcement learning. I graduated with at B.Sc. in Mathematics from MIT in 2013 and M.A.St. in Mathematics from the University of Cambridge in 2014. My other interests include creative writing, teaching, science fiction, and rationality.

Jason G.

I majored in applied math and computer science in USTC between 2010 and 2014 and joined the Statistical Machine Learning (SMiLe) lab at Princeton in Sept. 2014 for graduate study under the supervision of Prof. Han Liu. I worked on CUDA programming for real time rendering algorithm in USTC. In the summer of 2013, I developed a set of computer vision toolkits for microscopy video archive processing while working as a research intern at the Oxford Center for Applied Math. My recent research focuses on automatic feature engineering and variable selection in the presence of heavy noise and multicolinearity.

Karan S.

I'm a second year Ph.D., advised by Prof. Elad Hazan. My research is focused on the design of interactive learning algorithms involving feedback-driven data collection. My recent work deals with complex, structured decision-making systems, involving partial feedback, ubiquitous in online advertising, clinical decision making. I graduated from the Indian Institute of Technology, Kanpur in 2015 with the distinction of being awarded the President's Gold Medal for the best academic performance. In 2014, as a research intern at Microsoft Research, Redmond, I worked on Programming-by-Natural-Language techniques to translate natural language prompts into structured queries over knowledge bases.

Mikhail K.

I am an MSE student in the Department of Computer Science interested in developing algorithms and models for computational problems. My research has focused on machine learning, natural language processing, mathematical optimization, scientific computing, and partial differential equations. I received an A.B. in Mathematics with Honors from Princeton University in 2016. My thesis was supervised by Professor Sanjeev Arora.

Nikunj S.

I am a first year Masters student in the Computer Science department. I am interested in Machine Learning, deep learning and NLP.

Oluwatosin A.

I am currently a First-year Master's CS student. My undergraduate degree was in Electrical Engineering (summa cum laude) at The George Washington University. So the world of CS (especially AI) is relatively new to me. I find it interesting to learn about topics in different subject areas, and I am hoping to learn with and contribute to the Princeton team with my skills and persistence.

Sanjeev Arora - Faculty advisor

Professor of Computer Science, Princeton University. Interests include Theory, Algorithms, Machine Learning and NLP.

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US, NY, New York
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As an Applied Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
US, WA, Seattle
This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.
US, CA, Pasadena
The Amazon Center for Quantum Computing (CQC) team is looking for a passionate, talented, and inventive Research Engineer specializing in hardware design for cryogenic environments. The ideal candidate should have expertise in 3D CAD (SolidWorks), thermal and structural FEA (Ansys/COMSOL), hardware design for cryogenic applications, design for manufacturing, and mechanical engineering principles. The candidate must have demonstrated experience driving designs through full product development cycles (requirements, conceptual design, detailed design, manufacturing, integration, and testing). Candidates must also have a strong background in both cryogenic mechanical engineering theory and implementation. Working effectively within a cross-functional team environment is critical. Key job responsibilities The CQC collaborates across teams and projects to offer state-of-the-art, cost-effective solutions for scaling the signal delivery to quantum processor systems at cryogenic temperatures. Equally important is the ability to scale the thermal performance and improve EMI mitigation of the cryogenic environment. You will work on the following: - High density novel packaging solutions for quantum processor units - Cryogenic mechanical design for novel cryogenic signal conditioning sub-assemblies - Cryogenic mechanical design for signal delivery systems - Simulation-driven designs (shielding, filtering, etc.) to reduce sources of EMI within the qubit environment. - Own end-to-end product development through requirements, design reports, design reviews, assembly/testing documentation, and final delivery A day in the life As you design and implement cryogenic hardware solutions, from requirements definition to deployment, you will also: - Participate in requirements, design, and test reviews and communicate with internal stakeholders - Work cross-functionally to help drive decisions using your unique technical background and skill set - Refine and define standards and processes for operational excellence - Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
US, CA, Pasadena
The Amazon Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist specializing in the design of microwave components for use in cryogenic environments. Working alongside other scientists and engineers, you will design and validate hardware performing microwave signal conditioning at cryogenic temperatures for Amazon quantum processors. Working effectively within a cross-functional team environment is critical. The ideal candidate will have a proven track record of hardware development from requirements development to validation. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Key job responsibilities Our scientists and engineers collaborate across diverse teams and projects to offer state of the art, cost effective solutions for the signal conditioning of Amazon quantum processor systems at cryogenic temperatures. You’ll bring a passion for innovation, collaboration, and mentoring to: Solve layered technical problems across our cryogenic signal chain. Develop requirements with key system stakeholders, including quantum device, test and measurement, hardware, and theory teams. Design, implement, test, deploy, and maintain innovative solutions that meet both performance and cost metrics. Research enabling technologies necessary for Amazon reach commercial viability in quantum computing . A day in the life As you research, design, and implement cryogenic microwave signal conditioning solutions, you will also: Participate in requirements, design, and test reviews. Work cross-functionally to help drive decisions using your unique technical background and skill set. Define and maintain standards for operational excellence. Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly.
IT, Turin
As an Applied Scientist in the Alexa AI team, you will spearhead the advancement and deployment of state-of-the-art ML/RAG systems that revolutionize how millions of customers interact with Alexa. You'll leverage your expertise in machine learning, natural language processing, and large language models to create reliable, scalable, high-performance products that set new standards in operational excellence. Working at the intersection of research and production, you'll translate latest AI innovations into customer-facing features that delight users daily. Your work will span the full ML lifecycle—from analyzing customer behavior patterns and building novel metrics for personal digital assistants, to deploying automated training pipelines and conducting rigorous A/B testing across diverse devices and endpoints. Collaborating closely with business, engineering, and science teams across Amazon, you'll lead high-visibility programs that automate workflows and deliver measurable customer impact. This role offers the unique opportunity to publish at top-tier conferences while seeing your innovations scale to one of the world's most popular voice assistants, serving millions of customers globally. Key job responsibilities As an Applied Scientist in the Alexa AI team: - You'll analyze and model customer behavior at scale, building novel metrics for personal digital assistants across diverse devices and endpoints. Your work will involve creating deep learning, policy-based learning, and machine learning algorithms that directly impact customer experiences, translating complex data patterns into actionable insights that drive product innovation. - Your technical leadership will extend to building and deploying automated model training and evaluation pipelines, implementing complex machine learning and deep learning algorithms, and conducting rigorous model and data analysis through online A/B testing. You'll research and implement novel approaches that push the boundaries of what's possible in conversational AI. - Beyond model development, you'll ensure operational excellence by taking ownership of production systems, including on-call responsibilities during peak and non-peak hours. Working alongside Software Development Engineers, you'll deploy fixes and handle high-severity issues, ensuring our ML systems maintain the reliability and performance that millions of Alexa customers depend on daily. A day in the life As an Applied Scientist in the Alexa AI team, your day will involve collaborating with talented engineers and scientists to build scalable solutions for our conversational assistant. You'll dive into data analysis, experiment with novel algorithms, and iterate on models based on real-time user feedback. Working in a fast-paced, ambiguous environment, you'll tackle complex technical challenges—from debugging production issues to presenting research findings to stakeholders. Your self-motivated approach will drive you to swiftly deliver impactful solutions while maintaining the high standards that define our mission to revolutionize user experiences for millions of customers. About the team The Alexa AI team develops the intelligence behind one of the world's most popular voice assistants, serving millions of customers globally. We're a diverse group of scientists, engineers, and researchers united by our mission to make Alexa more natural, helpful, and delightful. Our culture thrives on innovation, collaboration, and customer obsession. We tackle some of the most challenging problems in conversational AI—from natural language understanding to personalization at scale. Here, you'll work alongside world-class talent, publish at top-tier conferences, and see your innovations impact customers daily. We move fast, think big, and celebrate both successes and learnings.
IT, Turin
As an Applied Scientist in the Alexa AI team, you will spearhead the advancement and deployment of state-of-the-art ML/RAG systems that revolutionize how millions of customers interact with Alexa. You'll leverage your expertise in machine learning, natural language processing, and large language models to create reliable, scalable, high-performance products that set new standards in operational excellence. Working at the intersection of research and production, you'll translate latest AI innovations into customer-facing features that delight users daily. Your work will span the full ML lifecycle—from analyzing customer behavior patterns and building novel metrics for personal digital assistants, to deploying automated training pipelines and conducting rigorous A/B testing across diverse devices and endpoints. Collaborating closely with business, engineering, and science teams across Amazon, you'll lead high-visibility programs that automate workflows and deliver measurable customer impact. This role offers the unique opportunity to publish at top-tier conferences while seeing your innovations scale to one of the world's most popular voice assistants, serving millions of customers globally. Key job responsibilities As an Applied Scientist in the Alexa AI team: - You'll analyze and model customer behavior at scale, building novel metrics for personal digital assistants across diverse devices and endpoints. Your work will involve creating deep learning, policy-based learning, and machine learning algorithms that directly impact customer experiences, translating complex data patterns into actionable insights that drive product innovation. - Your technical leadership will extend to building and deploying automated model training and evaluation pipelines, implementing complex machine learning and deep learning algorithms, and conducting rigorous model and data analysis through online A/B testing. You'll research and implement novel approaches that push the boundaries of what's possible in conversational AI. - Beyond model development, you'll ensure operational excellence by taking ownership of production systems, including on-call responsibilities during peak and non-peak hours. Working alongside Software Development Engineers, you'll deploy fixes and handle high-severity issues, ensuring our ML systems maintain the reliability and performance that millions of Alexa customers depend on daily. A day in the life As an Applied Scientist in the Alexa AI team, your day will involve collaborating with talented engineers and scientists to build scalable solutions for our conversational assistant. You'll dive into data analysis, experiment with novel algorithms, and iterate on models based on real-time user feedback. Working in a fast-paced, ambiguous environment, you'll tackle complex technical challenges—from debugging production issues to presenting research findings to stakeholders. Your self-motivated approach will drive you to swiftly deliver impactful solutions while maintaining the high standards that define our mission to revolutionize user experiences for millions of customers. About the team The Alexa AI team develops the intelligence behind one of the world's most popular voice assistants, serving millions of customers globally. We're a diverse group of scientists, engineers, and researchers united by our mission to make Alexa more natural, helpful, and delightful. Our culture thrives on innovation, collaboration, and customer obsession. We tackle some of the most challenging problems in conversational AI—from natural language understanding to personalization at scale. Here, you'll work alongside world-class talent, publish at top-tier conferences, and see your innovations impact customers daily. We move fast, think big, and celebrate both successes and learnings.
IT, Turin
As an Applied Scientist in the Alexa AI team, you will spearhead the advancement and deployment of state-of-the-art ML/RAG systems that revolutionize how millions of customers interact with Alexa. You'll leverage your expertise in machine learning, natural language processing, and large language models to create reliable, scalable, high-performance products that set new standards in operational excellence. Working at the intersection of research and production, you'll translate latest AI innovations into customer-facing features that delight users daily. Your work will span the full ML lifecycle—from analyzing customer behavior patterns and building novel metrics for personal digital assistants, to deploying automated training pipelines and conducting rigorous A/B testing across diverse devices and endpoints. Collaborating closely with business, engineering, and science teams across Amazon, you'll lead high-visibility programs that automate workflows and deliver measurable customer impact. This role offers the unique opportunity to publish at top-tier conferences while seeing your innovations scale to one of the world's most popular voice assistants, serving millions of customers globally. Key job responsibilities As an Applied Scientist in the Alexa AI team: - You'll analyze and model customer behavior at scale, building novel metrics for personal digital assistants across diverse devices and endpoints. Your work will involve creating deep learning, policy-based learning, and machine learning algorithms that directly impact customer experiences, translating complex data patterns into actionable insights that drive product innovation. - Your technical leadership will extend to building and deploying automated model training and evaluation pipelines, implementing complex machine learning and deep learning algorithms, and conducting rigorous model and data analysis through online A/B testing. You'll research and implement novel approaches that push the boundaries of what's possible in conversational AI. - Beyond model development, you'll ensure operational excellence by taking ownership of production systems, including on-call responsibilities during peak and non-peak hours. Working alongside Software Development Engineers, you'll deploy fixes and handle high-severity issues, ensuring our ML systems maintain the reliability and performance that millions of Alexa customers depend on daily. A day in the life As an Applied Scientist in the Alexa AI team, your day will involve collaborating with talented engineers and scientists to build scalable solutions for our conversational assistant. You'll dive into data analysis, experiment with novel algorithms, and iterate on models based on real-time user feedback. Working in a fast-paced, ambiguous environment, you'll tackle complex technical challenges—from debugging production issues to presenting research findings to stakeholders. Your self-motivated approach will drive you to swiftly deliver impactful solutions while maintaining the high standards that define our mission to revolutionize user experiences for millions of customers. About the team The Alexa AI team develops the intelligence behind one of the world's most popular voice assistants, serving millions of customers globally. We're a diverse group of scientists, engineers, and researchers united by our mission to make Alexa more natural, helpful, and delightful. Our culture thrives on innovation, collaboration, and customer obsession. We tackle some of the most challenging problems in conversational AI—from natural language understanding to personalization at scale. Here, you'll work alongside world-class talent, publish at top-tier conferences, and see your innovations impact customers daily. We move fast, think big, and celebrate both successes and learnings.
IT, Turin
As an Applied Scientist in the Alexa AI team, you will spearhead the advancement and deployment of state-of-the-art ML/RAG systems that revolutionize how millions of customers interact with Alexa. You'll leverage your expertise in machine learning, natural language processing, and large language models to create reliable, scalable, high-performance products that set new standards in operational excellence. Working at the intersection of research and production, you'll translate latest AI innovations into customer-facing features that delight users daily. Your work will span the full ML lifecycle—from analyzing customer behavior patterns and building novel metrics for personal digital assistants, to deploying automated training pipelines and conducting rigorous A/B testing across diverse devices and endpoints. Collaborating closely with business, engineering, and science teams across Amazon, you'll lead high-visibility programs that automate workflows and deliver measurable customer impact. This role offers the unique opportunity to publish at top-tier conferences while seeing your innovations scale to one of the world's most popular voice assistants, serving millions of customers globally. Key job responsibilities As an Applied Scientist in the Alexa AI team: - You'll analyze and model customer behavior at scale, building novel metrics for personal digital assistants across diverse devices and endpoints. Your work will involve creating deep learning, policy-based learning, and machine learning algorithms that directly impact customer experiences, translating complex data patterns into actionable insights that drive product innovation. - Your technical leadership will extend to building and deploying automated model training and evaluation pipelines, implementing complex machine learning and deep learning algorithms, and conducting rigorous model and data analysis through online A/B testing. You'll research and implement novel approaches that push the boundaries of what's possible in conversational AI. - Beyond model development, you'll ensure operational excellence by taking ownership of production systems, including on-call responsibilities during peak and non-peak hours. Working alongside Software Development Engineers, you'll deploy fixes and handle high-severity issues, ensuring our ML systems maintain the reliability and performance that millions of Alexa customers depend on daily. A day in the life As an Applied Scientist in the Alexa AI team, your day will involve collaborating with talented engineers and scientists to build scalable solutions for our conversational assistant. You'll dive into data analysis, experiment with novel algorithms, and iterate on models based on real-time user feedback. Working in a fast-paced, ambiguous environment, you'll tackle complex technical challenges—from debugging production issues to presenting research findings to stakeholders. Your self-motivated approach will drive you to swiftly deliver impactful solutions while maintaining the high standards that define our mission to revolutionize user experiences for millions of customers. About the team The Alexa AI team develops the intelligence behind one of the world's most popular voice assistants, serving millions of customers globally. We're a diverse group of scientists, engineers, and researchers united by our mission to make Alexa more natural, helpful, and delightful. Our culture thrives on innovation, collaboration, and customer obsession. We tackle some of the most challenging problems in conversational AI—from natural language understanding to personalization at scale. Here, you'll work alongside world-class talent, publish at top-tier conferences, and see your innovations impact customers daily. We move fast, think big, and celebrate both successes and learnings.
US, NY, New York
We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans. Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions. - Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation
GB, London
The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking an applied scientist with broad interest and expertise in model checking, interactive theorem proving, programming language semantics, and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - Work with customer teams to understand the nature of their software and the properties they need to establish of it. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use techniques spanning property-based testing to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team The Agentic Automated Reasoning Group at AWS develops and applies state of the art formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications, with a strong focus on AI based agents. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.