Machine Learning Software Engineer, Research

Machine Learning Software Engineer, Research

Physics

X, City of London, England, United Kingdom

Physics

X is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI‑driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high‑fidelity, multi‑physics simulation through AI inference across the entire engineering lifecycle, Physics

X unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

Note:

We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

• Work closely with our research scientists and simulation engineers to build and deliver models that address real‑world physics and engineering problems.
• Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain.
• Transform prototype model implementations to robust and optimised implementations.
• Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi‑node/multi‑GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on‑premise services.
• Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi‑GPU cloud compute.
• Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.
• Own research work‑streams at different levels, depending on seniority.
• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real‑world problems.
• Work at the intersection of data science and software engineering to translate the results of our research into reusable libraries, tooling and products.
• Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.

• Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.
• Ability to work autonomously and scope and effectively deliver projects across a variety of domains.
• Strong problem‑solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:
  • Scientific computing;
  • High‑performance computing (CPU / GPU clusters);
  • Parallelised / distributed training for large / foundation models.
• Ideally >1 year of experience in a data‑driven role, with exposure to:
  • Scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
  • Distributed computing frameworks (e.g., Spark, Dask) and high‑performance computing frameworks (MPI, OpenMP, CUDA, Triton);
  • Cloud computing (on hyper‑scalar platforms, e.g., AWS, Azure, GCP);
  • Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., Num Py, Sci Py, Pandas, PyTorch, JAX), especially including deep learning applications;
  • C/C++ for computer vision, geometry processing, or scientific computing;
  • Software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
  • Container‑ization and orchestration (Docker, Kubernetes, Slurm);
  • Writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

• Equity options – share in our success and growth.
• 10% employer pension contribution – invest in your future.
• Free…