Software Engineer - ML Robotics Systems

Software Engineer - ML Robotics Systems

About the Role

We’re working with a well-funded, highly technical team building next-generation AI systems that operate in the physical world.

They’re hiring engineers to work across the full stack of ML-powered robotics — from large-scale data pipelines to deploying models on real robots.

This is not a siloed role. You’ll move fluidly between infrastructure, machine learning, and real-world testing — owning problems end-to-end and seeing your work come to life outside of simulation.

On any given week, you might:

• Extend a multimodal (video-heavy) data pipeline
• Improve ML training or data-loading infrastructure
• Run experiments to validate model or system changes
• Deploy and test those changes directly on physical robots

If you enjoy building systems that actually interact with the real world, this is that kind of role.

What You’ll Work On

• Building and scaling distributed Python systems for data and ML workflows
• Designing pipelines to process massive volumes of multimodal data
• Improving infrastructure for large-scale model training and iteration
• Deploying and validating systems on real robotic platforms
• Driving performance, reliability, and scalability as systems grow 10×

What You’ll Own

• End-to-end systems that directly impact robot capability and behaviour
• Infrastructure that supports increasingly complex data and models
• Core logic that determines how robots learn from and use data
• Evolving the platform as it scales toward more general, capable systems

What They’re Looking For

• Experience building distributed systems or data pipelines at scale
• Strong Python skills and solid backend/infrastructure fundamentals
• Experience working with large-scale datasets (ideally video or multimodal)
• Understanding of modern ML systems and training workflows
• Experience with cloud infrastructure (e.g. Kubernetes, networking, permissions)

Nice to have:

• Experience at very large scale (e.g. petabyte-level data)
• Exposure to robotics, perception, or embodied AI systems

Why This Role Stands Out

• Your work directly impacts how real-world robotic systems behave
• Blend of ML, infrastructure, and hands-on deployment — not just one layer
• High ownership: take ideas from concept → production → real-world validation
• Work on genuinely hard problems at the intersection of AI and robotics
• Join a small, high-calibre team building at the frontier of embodied AI