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Machine Learning Engineer | Competitive Compensation | Algorithms
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Machine Learning Engineer
Want to build AI that operates in the real world - not just in a notebook?
We’re looking for an exceptional AI Engineer to develop and deploy state-of-the-art machine learning systems tackling complex problems across computer vision, multimodal sensing, robotics, and autonomy.
You’ll work end-to-end - from experimentation and model development through to deploying robust AI on cloud and resource-constrained edge hardware.
What you’ll do
- Design, train, and deploy deep learning models for challenging real-world problems
- Work across computer vision, multimodal AI, perception, and autonomy
- Take models from research and experimentation into production
- Build end-to-end AI systems spanning sensor data through to the user
- Optimise models for performance on edge and resource-constrained hardware
- Work with large real-world and synthetic datasets
- Collaborate with strong software engineers to integrate ML into production systems
- Explore new approaches where existing techniques aren't good enough
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We’d love to hear from people with experience in:
- Computer vision / robotics / autonomous systems
- Optimising models for GPUs or edge hardware
- Multimodal and vision-language models
- Synthetic data, simulation, or large-scale real-world datasets
- Production MLOps and model monitoring
- Research published at venues such as CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS, or TPAMI
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What’s on offer
- Highly competitive salary
- Generous equity
- 7% employer pension
- Private medical + dental insurance
- Free daily meals
- Relocation support
- Access to the equipment, resources, and training you need
- Significant ownership in an ambitious, technically demanding engineering team
This is an office-first London role and involves working directly with customers, including regular UK travel and occasional international deployments. You’ll need existing UK Right to Work and be willing and eligible to undergo UK security vetting (SC or above).
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