ML Robotics Systems Engineer
Compensation: $200-350k + equity
Location: Bay Area/Boston (Onsite)

The Company

This is a rare opportunity to join a well-funded, research-first robotics and embodied AI lab at an early but high-momentum stage — backed by tier-1 investors and strategic partners at the forefront of AI infrastructure and compute.

The founding team carries exceptional research pedigree, with alumni from some of the world's most respected AI organisations. Between the founders, they bring tens of thousands of academic citations in robotics and large-scale ML — representing some of the deepest technical credibility in the field globally.

The company's core thesis is that scaling real-world robot data, model size, and compute can unlock predictable, general improvements in robotic capability - analogous to what foundation models achieved for language and vision. The team is executing on this with both scientific rigour and real-world deployment on physical robots.

Backed by a major compute infrastructure partner alongside leading venture firms, the lab is well-resourced and operating with serious ambition. This is not incremental robotics = it's an attempt to reset what's possible.
 

This role sits at the intersection of machine learning, distributed systems, and physical robotics. You will tackle end-to-end problems that make AI models work better on real robots - spanning data pipelines, ML infrastructure, model training, and on-robot validation, all within the same scope of work.

One week you might be adding new functionality to a video processing pipeline; the next you could be updating the ML data loader, training models to validate a change, then testing on a physical robot. It requires stringing together distributed Python services for data and application processing, and marshalling large quantities of cloud infrastructure to handle that logic efficiently at scale.
 

• Design and implement new ideas that make the end-to-end system more robust, scalable, or faster
• Overhaul existing systems and services to handle the next 10x of scale
• Build and maintain complex distributed data pipelines, including large-scale video processing
• Write the business logic that gets the robot the data it needs, and gives customers the right access to robotic systems
• Coordinate large-scale cloud infrastructure to process workloads efficiently
• Span the full stack - from data ingestion through model training to real-world deployment on physical robots

• Extensive experience building complex distributed applications or data pipelines at scale
• Experience processing petabytes of data - bonus if that includes video data
• Strong Python expertise alongside solid distributed infrastructure skills
• Solid grounding in modern ML techniques with experience across large-scale training and production deployments
• Hands-on experience with distributed cloud infrastructure including cloud networking, permissions, and container orchestration (Kubernetes)
• Comfort moving across the full stack - debugging data issues, training models, and validating outcomes on real robots

The problems being solved here - building general-purpose embodied intelligence at scale - are among the most important and least-solved in AI today. You will have the autonomy to shape foundational research directions, work with some of the highest talent-density teams in robotics globally, and see your work deployed on real physical robots.

The culture is research-driven but execution-focused: deep scientific rigour combined with a relentless focus on what actually works in the real world. It is demanding, intense, and designed for people who want to work next to the best in the field.

If you are motivated by high-impact work, deep technical ownership, and the chance to help define what general-purpose robots can do.
 

Please apply via this listing or reach out directly. All applications are handled in strict confidence.