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Senior Research Machine Learning Engineer
Contract type
Ongoing
Work mode
Hybrid · London, United Kingdom
Experience
Senior · Not stated
Level inferred from job title
Job description
Machine learning engineer role at PhysicsX focused on turning physics and engineering prototypes into production-grade ML models, libraries and tooling, and on building scalable training and inference infrastructure on cloud and multi-GPU systems.
At a glance
- Employment
- Full-time
- Work mode
- Hybrid · London, United Kingdom
What you'll do
- Work closely with simulation engineers and research scientists to develop an understanding of the physics and engineering challenges being solved
- Work at the intersection of data science and software engineering to transform prototype implementations to robust production-grade model implementations, libraries and tooling
- Work with multi-GPU/multi-node distributed systems for training and inference at the scale of PBs of data and hundreds of GPUs
- Optimize the models and pipelines on all levels, from job scheduling down to GPU kernels
- Design and build scalable and robust infrastructure for end-to-end machine learning applications on cloud (CoreWeave, AWS)
- Continuously apply and improve engineering best practices and standards, coach colleagues in their adoption
What we're looking for
- Enthusiasm about using machine learning for science and engineering, and especially in scaling such solutions to real-world settings
- Ability to work autonomously in a fast-paced environment, learn and adapt quickly, scope, plan and deliver effectively
- Strong collaboration and communication skills, especially with research scientists
- Experience with software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)
- Experience building ML models and pipelines in Python, using common libraries and frameworks (e.g., PyTorch, TensorFlow, JAX)
- Experience with the GPU programming ecosystem, ability to optimise models on all levels of the stack, going down to the level of CUDA/Triton/Warp kernels when necessary
- Experience training and serving ML models at scale
- Experience with large-scale data processing
Nice to have
- Domain knowledge in physics and/or numerical methods, especially CFD
- Experience working in the domain of ML for science or engineering
- Experience building infrastructure on cloud platforms (e.g., AWS, Azure, GCP, CoreWeave)
- Experience with C/C++, especially for computer vision, geometry processing and scientific computing
Benefits
- Equity options
- 10% employer pension contribution
- Free office lunches
- Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave
- YellowNest nursery scheme
- 25 days of Annual Leave (+ Public Holidays)
- Private medical insurance - 100% employee cover
- Wellhub Subscription
- Eye tests
- Personal development
- Employee Assistance Programme (EAP)
- Bike2Work scheme
- Season ticket loan
- Octopus EV salary sacrifice
Apply
About PhysicsX
Re-architecting Engineering for the Age of Intelligence
PhysicsX is the physics AI company for industrials, building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges.
Mission
To accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today.
Key facts
- United Kingdom
- Physics AI / industrial engineering software
- Offices in London, New York, and Singapore with an expanding presence in the Bay Area
- Partners with organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables
- Flat structure where good ideas win
- Hybrid model blending time in the Shoreditch office with work-from-home days
- Sponsors bright women from disadvantaged backgrounds through their university degrees in science and mathematics
What they value
- Good ideas win - wherever they come from
- Questioning assumptions and challenging the status quo is expected
- Sustainable pace, long-term ambition
- Commitment to operating at the highest level
- Diversity and equal employment opportunity