Job description

About Dexterity At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work. We’re starting with warehouse automation, where the need for smarter, more resilient supply chains impacts millions of lives and businesses worldwide. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven and hardware-agnostic and have already picked 100+ million goods in production. And did we mention we’re customer-obsessed? Every decision, large and small, is driven by one question – how can we empower our customers with robots to do more than they thought was possible? Dexterity is one of the fastest-growing companies in robotics, backed by world-class investors such as Kleiner Perkins, Lightspeed Venture Partners, and Obvious Ventures. We’re a diverse and multidisciplinary team with a culture built on passion, trust, and dedication. Come join Dexterity and help make intelligent robots a reality! About the Role As a Senior/Staff Machine Learning Engineer, you will be working on a myriad of challenges related to robot task and action planning. You will leverage techniques from machine learning to solve hard sequential decision problems that require reasoning about the physical world and its dynamics. You will also stay abreast of the latest progress in imitation learning, reinforcement learning, and other related fields in order to further develop Dexterity’s technology foundations in Physical AI. Additionally, you will be responsible for updating and scaling our current ML pipelines to cover more scenarios and improve accuracy. Dexterity's robotic solutions integrate data from a multitude of sensors, including RGB cameras, depth sensors, force-torque sensors, encoders, system telemetry and human input. To better inform the planning algorithms, you may work on sensor fusion and state estimation techniques to leverage this multimodal sensory data. You will also work closely with the data platform, physics simulation, and robot operations teams to develop effective and efficient ways to improve the system’s internal world model. Dexterity has an expanding set of algorithmic challenges as we deploy new robotic applications, including areas such as: - Improving packing algorithms to build taller, denser, more stable structures with a wider variety of objects. - Solving the logistics task of moving and sorting inventory throughout a warehouse. - Building models that understand physics and geometry for both short- and long-horizon tasks. In addition to curating datasets and developing/improving machine learning models, you will be responsible for building data flywheels. Ideally, you will bring data-driven productization experience and help the team broadly in qualifying, deploying and updating models. Responsibilities Design and implement machine learning solutions across Dexterity’s robotics stack, including but not limited to perception, decision-making, action scoring, and predictive modeling Own the full ML development cycle for these solutions: data curation, labeling, training, evaluation, deployment, and iteration Build performant training and inference pipelines using PyTorch, with production-readiness and scalability in mind Collaborate closely with robotics, data platform, and simulation teams to integrate ML into real-time, latency-sensitive robotic systems Use profiling, monitoring, and experiments to optimize model performance and reliability Ensure reproducibility, traceability, and modularity across training and serving pipelines Maintain clean, production-quality code in Python (and C++ where required) Help establish best practices for model versioning, dataset management, and ML operations Required Skills Degree in Computer Science, Electrical Engineering, or Mathematics 5+ years of industry experience applying machine learning to real-world, production systemsStrong Python skills and deep experience with PyTorch Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction Strong engineering background with experience profiling, debugging, and optimizing model and pipeline performance Proven ability to design and maintain reliable systems, from model training to field deployment Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker) Familiarity with Linux, Git, CI/CD) and software development best practices (unit/acceptance/integration testing, code reviews) Nice to haves Prior experience in robotics, autonomous systems, or real-time ML applications Exposure to multimodal data (e.g., RGBD, force-torque, pose estimates, telemetry) Experience deploying models using serving stacks like NVIDIA Triton, TorchServe, or custom low-latency frameworks Background in computer vision, geometric learning, or time-series modeling Experience with Kubernetes, Ray or other distributed training and inference systems Previous startup experience or experience in fast-paced, cross-disciplinary environments Equal Opportunity Employer We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Requirements

Must-haves

You will also work closely with the data platform, physics simulation, and robot operations teams to develop effective and efficient ways to improve the system’s internal world model.
Dexterity has an expanding set of algorithmic challenges as we deploy new robotic applications, including areas such as:
Improving packing algorithms to build taller, denser, more stable structures with a wider variety of objects.
Solving the logistics task of moving and sorting inventory throughout a warehouse.
Building models that understand physics and geometry for both short- and long-horizon tasks.
Design and implement machine learning solutions across Dexterity’s robotics stack, including but not limited to perception, decision-making, action scoring, and predictive modeling
Own the full ML development cycle for these solutions: data curation, labeling, training, evaluation, deployment, and iteration
Build performant training and inference pipelines using PyTorch, with production-readiness and scalability in mind
Collaborate closely with robotics, data platform, and simulation teams to integrate ML into real-time, latency-sensitive robotic systems
Use profiling, monitoring, and experiments to optimize model performance and reliability
Ensure reproducibility, traceability, and modularity across training and serving pipelines
Maintain clean, production-quality code in Python (and C++ where required)
Help establish best practices for model versioning, dataset management, and ML operations
Degree in Computer Science, Electrical Engineering, or Mathematics 5+ years of industry experience applying machine learning to real-world, production systemsStrong Python skills and deep experience with PyTorch
Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction
Strong engineering background with experience profiling, debugging, and optimizing model and pipeline performance
Proven ability to design and maintain reliable systems, from model training to field deployment
Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker)

Nice-to-haves

Prior experience in robotics, autonomous systems, or real-time ML applications
Exposure to multimodal data (e.g., RGBD, force-torque, pose estimates, telemetry)
Experience deploying models using serving stacks like NVIDIA Triton, TorchServe, or custom low-latency frameworks
Background in computer vision, geometric learning, or time-series modeling
Experience with Kubernetes, Ray or other distributed training and inference systems
Previous startup experience or experience in fast-paced, cross-disciplinary environments

Application prep

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Keywords to mirror

machine learningaimlpythonawsgcpazuredockerci/cdkubernetes

Platform: lever
Effort: quick

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