Principal Engineer, AI​/ML Software

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The Dexterous AI Group (DAG) is working toward robots with human-level dexterity—the ability to manipulate objects with the versatility and adaptability people bring to everyday tasks. We combine tactile sensing with vision, robot learning, and control to advance that goal. We seek a hands-on Principal Robotics Software Engineer to lead software architecture across robot systems and the infrastructure used to develop, test, and deploy Physical AI.

You will set technical direction and remain hands-on while guiding engineers with different experience levels to deliver shared technical milestones.

You will own robotic manipulation use cases and define capabilities that demonstrate our solutions’ differentiation. You will lead the software and infrastructure supporting those capabilities across robot data collection and management, simulation and hardware experiments, model training, evaluation, and deployment. Shared tools and interfaces will let researchers compare ideas without rebuilding pipelines or robot integrations for each use case. Measurable success criteria and repeatable demonstrations will give customers and partners evidence of performance and integration requirements, while informing which capabilities merit further product investment.

• Own manipulation use cases from definition through demonstration.
• Define capabilities and measurable success criteria, validate differentiation against agreed baselines, and set the platform architecture and integration priorities needed to deliver.
• Turn ambiguous R&D goals into executable plans.
• Identify unknowns, use focused experiments to reduce risk, and make and document technical trade‑offs as evidence and priorities change.
• Lead delivery across engineers with varied experience.
• Break milestones into scoped work with clear ownership, dependencies, integration plans, and acceptance criteria; resolve blockers and communicate progress and risks early.
• Design, implement, and review core software in C++ and Python.
• Evolve the ROS 2-based stack and integrate hardware, sensing, control, simulation, and learned policies through maintainable interfaces.
• Bring production‑grade engineering to research software.
• Establish appropriate automated tests, continuous integration, hardware‑in‑the‑loop validation, observability and release practices, with measurable reliability and performance targets.
• Partner with AI and other software engineers to design data collection and replay, dataset versioning, simulation, training and evaluation workflows, and deployment tooling.
• Connect these through shared data formats and reproducible environments across robot and remote compute.
• Align software decisions with hardware, controls, and AI constraints.
• Address timing, synchronization, bandwidth, failure handling, and safe robot operation; distinguish exploratory prototypes from capabilities ready for sustained use.
• Develop the team through design and code reviews, practical coaching, and clear technical documentation.
• Facilitate constructive disagreement, adapt communication to the audience, and help engineers deliver independently.