£1,000 Per day
Outside
Onsite
London, England, United Kingdom
Summary: The Senior ML Infrastructure Engineer (Robotics) role involves designing, building, and optimizing training and inference platforms for advanced AI models in robotic systems. This hands-on position requires collaboration with researchers and ML engineers to ensure efficient deployment of robust systems across various environments. The engineer will focus on the full machine learning lifecycle, addressing complex systems challenges that impact real-world applications. The role is primarily onsite in London and offers a contract outside IR35.
Key Responsibilities:
- 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
Key Skills:
- 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 (nice to have)
- Exposure to low-level systems concepts such as multithreading, networking, or memory management (nice to have)
- Experience with ML performance optimisation or compilers (nice to have)
- Background in reinforcement learning, VLA models, or embodied AI systems (nice to have)
- Experience working in cloud environments such as AWS, GCP, or Azure (nice to have)
Salary (Rate): £1,000.00/daily
City: London
Country: United Kingdom
Working Arrangements: on-site
IR35 Status: outside IR35
Seniority Level: Senior
Industry: IT
Senior ML Infrastructure Engineer (Robotics) London Contract, Outside IR35 £600-1000 per day Primarily onsite
Overview
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.
The Role
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.
Key Responsibilities
- 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
Required Experience
- 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
Nice to Have
- 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
Why Apply
- 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
To Apply, Please email:
Desired Skills and Experience ML infrastructure, distributed training, inference optimisation, robotics AI, Python, C++, PyTorch, JAX, distributed systems, cloud computing