Machine Learning Software Engineer, Research

Overview

About Us 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 across different levels, 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 re-usable 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 >2 years of experience in a data-driven role in a professional setting, 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-scaler 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.

• Build what actually matters – Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for…