Machine Learning Engineer (Autonomous Systems)

Machine Learning Engineer (Autonomous Systems)📍 London | Liverpool Street | Hybrid – 3 days per weekWant 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.What’s in it for you?Deploy ML onto real autonomous platformsWork with imagery, LiDAR, telemetry and sensor dataTake models from experimentation through to real-world deploymentWork closely with ML, hardware and systems engineersTackle problems across deep learning, computer vision and embedded AIBuild systems where performance, reliability and efficiency genuinely matterWhat you’ll be doingThe 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 modelsDeveloping computer vision and vision-language-action architecturesOptimising models for constrained and embedded hardwareBuilding ML training and inference infrastructureWorking with GPU clusters, cloud, Docker and KubernetesEngineering large-scale geospatial and sensor datasetsUsing simulation and synthetic data to improve model performanceWhat you’ll bringYou don’t need to tick every box. Depth in one area is more valuable than surface-level experience across all of them.You’ll likely have:Strong experience in ML Engineering, MLOps or Data EngineeringSolid programming skills in Python, C++ or RustExperience taking complex ML or data systems into productionA good understanding of modern ML development and deploymentExperience with computer vision, infrastructure, embedded ML or large-scale data would be particularly relevantIf 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.