Alexa Prize TaskBot Challenge Finals

Alexa Prize TaskBot Challenge

Three top performers emerge in inaugural Alexa Prize TaskBot Challenge—the first conversational AI challenge to incorporate multimodal (voice and vision) customer experiences.

Three university teams have emerged as top performers in the inaugural Alexa Prize TaskBot Challenge.

Launched in March 2021, the TaskBot Challenge involved ten university teams who competed to develop bots that could assist customers in completing cooking or do-it-yourself home improvement tasks that required multiple steps and decisions.

The teams’ goal: build taskbots that assist customers in multi-step tasks, such as baking a birthday cake or fixing a scratch on a car — and adapt those instructions based on the resources and tools available to the customer. If, for example, a customer ran out of an ingredient halfway through a recipe or didn’t have a specific tool for a DIY project, the taskbot had to adjust the plan and suggest possible solutions.

Customers interacted with the taskbots by initiating an interaction, after the interaction ended, customers were asked to rate — on a scale from 1 to 5 — how helpful that taskbot was with the task.

“This challenge was motivated by our north star that Alexa will keep inventing next-generation conversational AI experiences that address our customers’ changing needs,” said Yoelle Maarek, vice president of research and science, Alexa Shopping. “Congratulations to our top-performing teams, and to all of the teams who participated in this inaugural challenge. We were delighted with the high level of engagement from the academic community, and by the advances the teams made against the research directions established for the challenge.”

The TaskBot Challenge is the first conversational AI challenge to incorporate multimodal customer experiences; in addition to receiving verbal instructions, customers were also presented with images or diagrams to guide them through the task.

Success required the teams to address many difficult AI challenges, from knowledge representation and inference, and commonsense and causal reasoning, to language understanding and generation, requiring fusion of multiple AI techniques.

The GRILL team from the University of Glasgow consists of, left to right, Iain Mackie, Federico Rossetto (pictured on tablet), Paul Owoicho (standing), Carlos Gemmell, Jeff Dalton (standing), and Sophie Fischer.
The GRILL team from the University of Glasgow consists of, left to right, Iain Mackie, Federico Rossetto (pictured on tablet), Paul Owoicho (standing), Carlos Gemmell, Jeff Dalton (standing), and Sophie Fischer.

Each of the ten teams participating in the challenge received $250,000 research grants, Alexa-enabled devices, free Amazon Web Services cloud computing services to support their research and development efforts, access to the TaskBot Toolkit, other data resources, and Alexa team support.

Five teams were selected for the finals earlier this year.

NOVA School of Science and Technology - Team TWIZ team photos
The team from NOVA School of Science and Technology (FCT NOVA) in Portugal earned second place with their bot, TWIZ.

GRILLBot, a “multi-modal task-oriented digital assistant to guide users through complex real-world tasks” developed by a team of graduate students at the University of Glasgow, emerged as the winner of the challenge.

A team from the NOVA School of Science and Technology (FCT NOVA)  in Portugal earned second place with their bot, Twiz, and a team from Ohio State University (OSU) earned third-place honors with its TacoBot. The finalists also included QuakerBot from the University of Pennsylvania (UPenn) and GauchoBot from the University of California, Santa Barbara (UCSB).

The top performers earned prize money to be divided among the team members: $500,000 for first place, $100,000 for second, and $50,000 for third.

“The experience of taking the ambitious Alexa Prize TaskBot goals from ideas and research to prototype and launching on a tight timeline was thrilling and humbling,” said Federico Rossetto, a PhD student and one of the GRILL team members.

The Ohio State Universiy TacoBot team consists of, left to right, top row: Zhen Wang and Samuel Stevens;  second row: Tianshu Zhang, Shijie Chen, and Yu Su; third row: Lingbo Mo, Xiang Yue, Xiang Deng; and bottom row: Ashley Lewis, Ziru Chen, and Huan Sun.
The Ohio State Universiy TacoBot team consists of, left to right, top row: Zhen Wang and Samuel Stevens; second row: Tianshu Zhang, Shijie Chen, and Yu Su; third row: Lingbo Mo, Xiang Yue, Xiang Deng; and bottom row: Ashley Lewis, Ziru Chen, and Huan Sun.

Another GRILLBot team member said the team realized it was essential to provide a wide variety of images and videos that help illustrate the techniques involved in each task.

“We learned that users learn and enjoy a task more when there are rich image and video elements,” said Iain Mackie, a PhD student. To do that, the students relied on image and video augmentations, applying multimedia elements that could be reusable across different tasks.

“Ultimately, the goal of taskbots is one shared by the research vision from our research lab: to enable people to understand the world and make people's lives better,” said Carlos Gemmell, GRILLBot’s project leader.

TWIZ, the taskbot developed by the FCT NOVA team, introduced an element of curiosity to the challenge. The bot would mention curious facts related to the tasks being performed, which increased customer engagement.

One of the lessons learned by the team was the limits of some algorithms when faced with human unpredictability.

“Transformer-based algorithms work extremely well in controlled settings, but in conversational settings it is natural for users to ramble until they find what they really want,” noted Rafael Ferreira, a PhD student and FCT NOVA‘s team leader. “This creates many new exploratory behaviors which can lead to conversations that must be fun and engaging.”

OSU TacoBot team members said they learned about the importance of user experience in developing real-life dialogue systems.

“User engagement is a key aspect to consider in developing taskbots. Instead of only focusing on the task, users seem to have an interest in having a chit-chat with the bot from time to time. How to naturally transition between chit-chat and task-oriented turns requires prolonged efforts,” said Huan Sun, an associate computer science and engineering professor at OSU, who served as the team’s faculty advisor.

All 10 teams participating in the competition have published research papers detailing their work.

“The TaskBot Challenge, launched last year, is among three Alexa Prize challenges,” explained Reza Ghanadan, a senior principal research scientist who oversees Amazon’s Alexa Prize competitions. “Our original Alexa Prize competition, SocialBot Grand Challenge, just completed its fourth challenge and we plan to announce the next competition, SGC5, in late June 2022, and earlier this year we introduced the SimBot Challenge, which will soon enter its live interactions phase with ten university teams competing. We are excited about how each of these challenges is contributing to the advancement of human-computer interactions through conversations.”

The inaugural TaskBot Challenge was the first of three that will occur over a three-year period. The second challenge will launch in September 2022.

Research areas

Latest news

The latest updates, stories, and more about Alexa Prize.
US, MA, Boston
We're a new research lab based in San Francisco and Boston focused on developing foundational capabilities for useful AI agents. We're pursuing several key research bets that will enable AI agents to perform real-world actions, learn from human feedback, self-course-correct, and infer human goals. We're particularly excited about combining large language models (LLMs) with reinforcement learning (RL) to solve reasoning and planning, learned world models, and generalizing agents to physical environments. We're a small, talent-dense team with the resources and scale of Amazon. Each team has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. AI agents are the next frontier—the right research bets can reinvent what's possible. Join us and help build this lab from the ground up. Key job responsibilities * Define the product vision and roadmap for our agentic developer platform, translating research into products developers love * Partner deeply with research and engineering to identify which capabilities are ready for productization and shape how they're exposed to customers * Own the developer experience end-to-end from API design and SDK ergonomics to documentation, sample apps, and onboarding flows * Understand our customers deeply by engaging directly with developers and end-users, synthesizing feedback, and using data to drive prioritization * Shape how the world builds AI agents by defining new primitives, patterns, and best practices for agentic applications About the team Our team brings the AGI Lab's agent capabilities to customers. We build accessible, usable products: interfaces, frameworks, and solutions, that turn our platform and model capabilities into AI agents developers can use. We own the Nova Act agent playground, Nova Act IDE extension, Nova Act SDK, Nova Act AWS Console, reference architectures, sample applications, and more.
US, CA, San Francisco
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics 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 — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - 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 group 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.
US, NY, New York
Do you want to lead the Ads industry and redefine how we measure the effectiveness of Amazon Ads business? Are you passionate about causal inference, Deep Learning & AI, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be a science leader within our Advertising Incrementality Measurement science team! Our work builds the foundations for providing customer-facing advertising measurement tools, furthering internal research & development, and building out Amazon's advertising measurement offerings. Incrementality is a lynchpin for the next generation of Amazon Advertising measurement solutions, and this role will play a key role in the release and expansion of these offerings. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and/or econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers to define key business questions and work with the engineering team to bring our solutions into production. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon advertising, and also allow you to become part of our large science community. Key job responsibilities • Apply expertise in ML/DL, AI, and causal modeling to develop new models that describe how advertising impacts customers’ actions • Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions • Improve upon and simplify our existing solutions and frameworks • Review and audit modeling processes and results for other scientists, both junior and senior • Work with leadership to align our scientific developments with the business strategy • Identify new opportunities that are suggested by the data insights • Bring a department-wide perspective into decision making • Develop and document scientific research to be shared with the greater science community at Amazon About the team AIM is a cross disciplinary team of engineers, product managers, economists, data scientists, and applied scientists with a charter to build scientifically-rigorous causal inference methodologies at scale. Our job is to help customers cut through the noise of the modern advertising landscape and understand what actions, behaviors, and strategies actually have a real, measurable impact on key outcomes. The data we produce becomes the effective ground truth for advertisers and partners making decisions affecting millions in advertising spend.
US, CA, San Francisco
In this role, you will act as the primary specialist for physics engine internals and dynamics, developing high-fidelity, vectorized simulation environments for robotics locomotion, navigation, and interaction/manipulation. You will collaborate with hardware engineers to validate robot models and partner with research scientists to ensure numerical stability and physical accuracy for Sim2Real transfer. Your work focuses on tuning solvers, optimizing collision dynamics, and performing system identification to enable the training of robust robot control policies for complex, physical interactions. Key job responsibilities * Develop and maintain the shared simulation software framework, specifically owning the physics integration, robot state management, and control layers * Develop and optimize parallelized (vectorized) physics environments for high-throughput reinforcement learning (e.g., Isaac Lab, MuJoCo) * Tune physics engine parameters (solvers, friction, restitution) to support complex contact-rich scenarios required for dexterous manipulation and agile locomotion. * Implement and validate complex robot models (URDF/MJCF) involving precise actuator and sensor modeling * Collaborate with robot engineers and scientists to perform System Identification (SysID) to minimize the Sim2Real gap About the team At Frontier AI & Robotics (FAR), 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 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.
CA, BC, Vancouver
The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
CA, ON, Toronto
The Brand Registry team is seeking an Applied Scientist to tackle complex, high-impact problems that directly affect millions of brands, selling partners, and customers on Amazon. You will design, develop, and deploy AI solutions—leveraging large language models (LLMs) and agentic AI frameworks—to power intelligent automation that augments human decision-making and drives autonomous outcomes at scale. What You'll Do -Build agent-based AI systems that reason, plan, and act like domain experts progressing from decision-support tools to fully autonomous solutions -Own the end-to-end ML lifecycle, from problem formulation and data analysis through experimentation, model development, and production deployment -Work backwards from data insights and customer feedback to identify the highest-value science opportunities and translate them into scalable machine learning solutions -Partner closely with product managers and engineering teams to define requirements, iterate rapidly, and launch solutions that deliver measurable business impact -Collaborate with domain experts across Amazon to pioneer innovative approaches to unsolved problems in brand protection and seller experience What We're Looking For -Technical depth: Extensive hands-on experience in Machine Learning, with a strong focus on Generative AI and LLM-based applications (e.g., fine-tuning, prompt engineering, retrieval-augmented generation, multi-agent orchestration) -End-to-end delivery: Proven track record of driving large-scale ML initiatives from conception through production launch in fast-paced, ambiguous environments -Scientific rigor: Strong foundation in experimental design, statistical analysis, and the ability to translate research into production-grade systems -Customer obsession: A bias toward working backwards from real-world problems and customer pain points rather than technology for its own sake -Entrepreneurial mindset: Comfort with ambiguity, a bias for action, and the tenacity to break down complex problems into actionable solutions -Communication skills: Ability to articulate technical concepts clearly to both technical and non-technical stakeholders About the team Brand Registry's mission is bold and unambiguous: protect 100% of the brands in the Amazon catalog. We are the team that stands between authentic brands and the forces that threaten their integrity — counterfeit products, catalog abuse, unauthorized sellers, and inaccurate brand representation. We do this by building the tools, systems, and experiences that empower brand owners to establish, protect, and grow their presence on Amazon with confidence. Achieving this mission requires deep collaboration across science, engineering, legal, and selling partner experience teams — all working in concert to deliver a seamless, trustworthy brand ownership experience at global scale.
US, CA, San Francisco
The Amazon AGI SF Lab is focused on developing new foundational capabilities for enabling useful AI agents that can take actions in the digital and physical worlds. We’re enabling practical AI that can actually do things for us and make our customers more productive, empowered, and fulfilled. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Our philosophy combines the agility of a startup with the resources of Amazon. By keeping the team lean, we’re able to maximize the amount of compute per person. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. In this role, you will work closely with research teams to design, build, and maintain systems for training and evaluating state-of-the-art agent models. Our team works inside the Amazon AGI SF Lab, an environment designed to empower AI researchers and engineers to work with speed and focus. Our philosophy combines the agility of a startup with the resources of Amazon. Key job responsibilities * Develop training infrastructure to ensure large-scale reinforcement learning on LLMs runs highly efficient and robust. * Work across the entire technology stack, including low level ML system, job orchestration and data management. * Analyze, troubleshoot and profiling complex ML systems, identify and address performance bottlenecks. * Work closely with researchers, conduct MLSys research to create new techniques, infrastructure, and tooling around emerging research capabilities.
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
US, WA, Seattle
Amazon is seeking exceptional science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems. As a Senior Research Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput. The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation. Key job responsibilities - Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented - Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production - Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization - Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible - Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments - Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone - Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities - Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality - Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations - Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver - Write clear narratives and documentation describing scientific solutions and design choices