Software Engineer V – Applied ML Research

Spearhead Robotic Grasping: Take end-to-end ownership of the intelligence driving our robots' physical interactions, dynamically determining optimal gripper configurations and tool selections for diverse, complex objects.

Bridge Research and Reality: Translate cutting-edge machine learning research into robust production systems. You will modify novel architectures, orchestrate training pipelines, deploy directly to our robotic fleet, and drive continuous improvement based on real-world field telemetry.

Architect Ground-Truth Evaluation: Design and implement rigorous evaluation infrastructure that proves model efficacy in live, chaotic environments (like real trailers) — moving beyond static benchmarks to guarantee true operational performance.

Pioneer Simulation-First Development: Engineer and validate sophisticated grasping and manipulation strategies within high-fidelity simulations to ensure flawless execution before deploying to physical hardware.

Drive Cross-Functional Technical Vision: Collaborate closely with perception and motion planning experts, setting the technical bar through rigorous code reviews, innovative design discussions, and collaborative debugging.

Leverage Force-Multiplying Workflows: Utilize advanced AI coding assistants and modern development paradigms to accelerate engineering velocity, allowing you to achieve outsized impact and move faster than traditional teams.

Proven ML Track Record: 6+ years of experience shipping end-to-end machine learning systems — spanning data pipelines, training, evaluation, and deployment — and maintaining ownership in production. (An MS degree counts as two years of experience).

Applied Robotics Expertise: Demonstrated success deploying machine learning models within complex, active physical robotic systems.

Architectural Depth: Deep expertise in bespoke model design. You don't just rely on off-the-shelf APIs; you possess the ability to fundamentally reshape neural networks to solve unique, domain-specific challenges.

Cloud ML Proficiency: Fluency and comfort in architecting and running medium-to-large-scale training pipelines in cloud environments.

Engineering Excellence: Mastery of Python and modern software engineering best practices.

Foundational Robotics Knowledge: A solid grasp of classical robotics concepts, including motion planning, kinematics, geometry, and perception.

Academic Foundation: A BS or higher in Robotics, Computer Science, or a closely related technical field.

Cross-Disciplinary Communication: The ability to distill complex ML concepts and collaborate effectively with engineers across different specialized domains.

Local Collaboration: Ability to work from our dynamic Charlestown, MA headquarters at least 3 days a week in a hybrid capacity.