Machine Learning Engineer – Physical AI / Robotics
As a founding-stage ML engineer, you will train vision-language-action models and world models, working in Python with modern ML frameworks to build training and evaluation pipelines. You will deploy these models on real robotic hardware, covering the full ML loop from data infrastructure to hardware testing.
Free Tailor for ATS: 10/10 runs left
Machine Learning Engineer – Physical AI / Robotics
What if the models you trained didn’t just generate an answer, but made a robot move?
This is an opportunity to work at the intersection of machine learning, robotics and Physical AI, building models that learn how to interact with the real world.
You’ll join an ambitious UK AI startup at an early enough stage to have genuine influence over the technology, working on everything from large-scale model training to evaluation, data and deployment on real robotic hardware.
What’s in it for you?
Founding-stage engineering role with meaningful equity
Train vision-language-action models and world models
See your models tested on real robots, not just benchmarks
Work across the full ML loop: data → training → evaluation → deployment
Build infrastructure that makes every training iteration faster and more effective
Significant technical ownership without layers of process or bureaucracy
Help shape the ML foundations of a company tackling one of AI’s hardest problems
Your work will include:
Training and fine-tuning large-scale machine learning models
Building high-performance training and evaluation pipelines
Developing the data infrastructure needed for rapid experimentation
Improving dataset quality and creating better feedback loops
Optimising model performance, training efficiency and iteration speed
Evaluating models on real robotic systems
Using results from hardware testing to inform the next training cycle
The core experience we’re looking for is:
Strong experience training deep learning models
Excellent Python skills and experience with modern ML frameworks
A strong understanding of model training, optimisation and performance
Experience building reliable ML training or evaluation infrastructure
Solid mathematical foundations and a rigorous approach to experimentation
The ability to diagnose why a model isn’t working and systematically improve it
An appetite for the ownership and ambiguity that comes with an early-stage company
You don’t need to have spent your career in robotics. What matters is that you’re a strong ML engineer who wants to work on models that perceive, reason and act in the physical world.