Senior ML Infrastructure Engineer (Robotics) in London

We are partnering with a robotics and AI company building the core software infrastructure that enables advanced AI models to operate reliably in real-world robotic systems. They are looking for a Senior ML Infrastructure Engineer to design, build, and optimise the training and inference platforms at the heart of their technology.

This is a hands‐on engineering role focused on scale, performance, and reliability. You will work across the full machine‐learning lifecycle, from distributed training pipelines to highly optimised inference systems deployed into production robotics environments.

You will join a highly technical team working at the intersection of software engineering, machine‐learning infrastructure, and robotics. Your focus will be on turning cutting‐edge models into robust, production‐ready systems that run efficiently across cloud and constrained hardware environments. You will collaborate closely with researchers and ML engineers, help shape architectural decisions, and solve complex systems problems that directly impact real‐world deployment.

• Design, build, and maintain distributed ML training pipelines including data preprocessing, orchestration, training, and evaluation.
• Optimise training performance, scalability, and resource utilisation across online and offline learning workflows.
• Develop and optimise AI inference pipelines for deployment into real‐world robotic systems.
• Build infrastructure and tooling to support reliable model integration, monitoring, and production analysis.
• Implement optimisation strategies across inference paradigms, including autoregressive, denoising, hierarchical, and multi‐agent systems.
• Work across cloud and hardware constraints to ensure models run efficiently in production.
• Contribute to high engineering standards through testing, CI, and robust system design.

• Strong software engineering background with Python and/or C++.
• Hands‐on experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building or maintaining ML infrastructure, training platforms, or inference systems.
• Solid understanding of distributed systems, parallel computing, and large‐scale data processing.
• Strong fundamentals in algorithms, data structures, and system design.

• Experience optimising ML inference for robotics, edge, or embedded systems.
• Exposure to low‐level systems concepts such as multithreading, networking, or memory management.
• Experience with ML performance optimisation or compilers.
• Background in reinforcement learning, VLA models, or embodied AI systems.
• Experience working in cloud environments such as AWS, GCP, or Azure.

• Work on real‐world robotics systems rather than research‐only models.
• Solve complex engineering problems with direct impact on deployed products.
• Based in London, with a highly technical, collaborative team.

• ML infrastructure, distributed training, inference optimisation, robotics AI, Python, C++, PyTorch, JAX, distributed systems, cloud computing.