💰 £80-160k base salary + equity

I’m partnering with a venture-backed Physical AI startup building models that enable robots to learn and act in the real world. They’re hiring their first dedicated ML Engineer to build the training, evaluation and data pipelines behind their robotic systems.

You’ll have robots on your desk, fast experimentation loops, and ownership of taking models from training through to deployment on real hardware.

What you’ll work on:

• Fine-tuning VLA and world-model checkpoints, progressing towards training models from scratch.

• Accelerating training through efficient dataloaders, sharding, parameter-efficient methods and experiment sweeps.

• Building evaluation harnesses covering held-out metrics, simulation rollouts and policy regression testing.

• Developing robot data pipelines, augmentation and checks that catch data issues before training.

• Deploying models onto physical robots and using the results to guide the next experiment.

Who could be a fit?

Engineers who have trained models hands-on. Experience with VLAs or world models is ideal, but backgrounds in LLMs, VLMs, diffusion, or large-scale ranking and recommendation are also relevant.

Strong mathematical reasoning matters. Maths, physics, quant and competition backgrounds are welcome-you don’t need an existing ML job title.

Particularly interested in people with experience across:

• Model training and optimisation, with a focus on improving iteration speed.

• Robotics simulation and evaluation, including NVIDIA Isaac Sim.

• Unitree G1 robots, VLM orchestration, or deploying learned policies on physical hardware.

📍 Bermondsey, London - fully onsite

💰 £80-160k base salary + equity

🔬 First dedicated ML hire with ownership from day one

Interested? Apply here or drop me your resume over to khorsley@acceler8talent.com

Hiring #MachineLearningEngineer #PhysicalAI #RobotLearning #Robotics #LondonJobs

Engineers who have trained models hands-on. Experience with VLAs or world models is ideal, but backgrounds in LLMs, VLMs, diffusion, or large-scale ranking and recommendation are also relevant.

Strong mathematical reasoning matters. Maths, physics, quant and competition backgrounds are welcome-you don’t need an existing ML job title.

Particularly interested in people with experience across:

• Model training and optimisation, with a focus on improving iteration speed.

• Robotics simulation and evaluation, including NVIDIA Isaac Sim.

• Unitree G1 robots, VLM orchestration, or deploying learned policies on physical hardware.