D
Dexterity
Senior/Staff Machine Learning Engineer
Skills
Google Cloud PlatformDockerPythonPyTorchMachine LearningAIAWSLinuxKubernetesAzure
2 months ago
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Important information
Contract type:
Permanent
Salary:
$170,000 – $225,000 / year
Location:
Redwood City, CA, USA
Work mode:
Onsite
Published on:
29 August 2026
What they need
- 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
- 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- Ideally, you will bring data-driven productization experience and help the team broadly in qualifying, deploying and updating models
- Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker)
- 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
- Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction
- Familiarity with Linux, Git, CI/CD) and software development best practices (unit/acceptance/integration testing, code reviews)
- 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
- 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
- Experience with Kubernetes, Ray or other distributed training and inference systems
- Background in computer vision, geometric learning, or time-series modeling
- Previous startup experience or experience in fast-paced, cross-disciplinary environments
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