London | Liverpool Street | Hybrid – 3 days per week
Want to build ML that goes beyond a benchmark and actually operates in the real world?
This is an opportunity to develop and deploy machine learning for advanced autonomous systems, working with everything from computer vision and deep learning to sensor data, embedded AI and large-scale ML infrastructure.
Whats in it for you?
- Deploy ML onto real autonomous platforms
- Work with imagery, LiDAR, telemetry and sensor data
- Take models from experimentation through to real-world deployment
- Work closely with ML, hardware and systems engineers
- Tackle problems across deep learning, computer vision and embedded AI
- Build systems where performance, reliability and efficiency genuinely matter
What youll be doing
The team is growing across ML Engineering, MLOps and Data Engineering, so the exact focus can play to your strengths.
Depending on your background, you could be:
- Training and optimising deep learning models
- Developing computer vision and vision-language-action architectures
- Optimising models for constrained and embedded hardware
- Building ML training and inference infrastructure
- Working with GPU clusters, cloud, Docker and Kubernetes
- Using simulation and synthetic data to improve model performance
What youll bring
You dont need to tick every box. Depth in one area is more valuable than surface-level experience across all of them.
Youll likely have:
- Strong experience in ML Engineering, MLOps or Data Engineering
- Solid programming skills in Python, C++ or Rust
- Experience taking complex ML or data systems into production
- A good understanding of modern ML development and deployment
- Experience with computer vision, infrastructure, embedded ML or large-scale data would be particularly relevant
If you want to work on ML that has to perform outside the lab, get in touch and I’ll share more about the team, technology and projects.
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