Location:
Cambridge
|
Full-time
ML Engineer - Simulation & Robotics
As a Machine Learning Engineer – Simulation & Robotics, you will design and build simulation infrastructure to
accelerate robot learning. Your work will enable rapid iteration on learning-based approaches by reducing
dependence on physical robot evaluations, constructing realistic and scalable simulations, and generating large-scale
training data. You will help bridge the gap between simulated and real robot behaviour, supporting training and
evaluation efforts and pushing the boundaries of embodied AI.
RESPONSIBILITIES
- Own key parts of the robot learning pipeline, with responsibility for the performance and reliability of learned behaviours on physical robots.
- Develop and train learning-based robotic policies using reinforcement learning, imitation learning, and other relevant approaches.
- Build and improve simulation and sim-to-real workflows, including domain randomisation, system identification, and robustness-driven training.
- Deploy and iterate on learned policies on physical robots, diagnosing differences between simulated and real-world behaviour.
- Work closely with the wider engineering team to integrate learned behaviours into physical robotic systems.
- Develop evaluation methods and tooling to measure policy performance, robustness, and real-world behaviour.
QUALIFICATIONS
- The areas below reflect the broader scope of the role. We don't expect candidates to have deep experience in all of them, but we look for strong foundations, relevant strengths, and the ability to develop quickly in less familiar areas.
- Demonstrated experience building and evaluating learning-based robotics systems. Candidates with 3+ years at this level may be considered for a senior role.
- MS or PhD in Computer Science, Robotics, or a closely related field is preferred.
- Strong Python and software engineering skills are essential, with experience developing, debugging, and extending machine learning or robotics systems.
- Strong foundations and practical experience in machine learning, particularly in areas relevant to robot learning such as reinforcement learning, imitation learning, or policy learning.
- Experience developing and training learned policies, ideally for robotic or embodied systems.
- Experience in one or more relevant areas such as robotics simulation, sim-to-real, domain randomisation, system identification, or robustness-driven training.
- Familiarity with PyTorch or an equivalent machine learning framework.
- Strong systems-level debugging and problem-solving ability, with the ability to investigate issues across learning, simulation, software, and physical behaviour.
- Demonstrated ability to quickly develop proficiency in unfamiliar technical areas and apply new knowledge to engineering problems.
- Experience deploying and iterating learned policies on physical robots would be particularly valuable.
BENEFITS
- Work at the frontier of physical AI, with direct access to advanced robot hardware and edge compute.
- Significant technical ownership in a small, highly experienced team.
- Competitive salary commensurate with experience.
- 25 working days of holiday per year, in addition to public holidays.
- Visa sponsorship.
If you’re interested in this opportunity, we’d love to hear from you.
Please get in touch at careers@v-sim.co.uk.
