Machine Learning Software Engineer, Research

Overview

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.

• Shape Research group strategy and culture in a significant way, especially in domains of expertise.
• Be opinionated and formulate strategy on engineering topics relevant to our Research priorities, especially on: scaled engineering, securing compute, infrastructure stack.
• Define necessary profiles to execute this strategy.
• Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment.
• Nurture younger colleagues to grow their skillset and guide their professional development.
• Own Research work-streams at a high-level to deliver outcomes.
• Align priorities with problem stakeholders, internal and external.
• Set the technical direction for the stream and apply judgement and taste to drive progress.
• Plan roadmaps with clear milestones for key decisions and outcomes.
• Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap.
• Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment.
• The below activities in particular.
• 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.
• 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.
• 4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in most of the below:
• scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
• employing distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);
• employing 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;
• building or using C/C++ for computer vision, geometry processing, or scientific computing;
• following and promoting software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
• container-izing and orchestrating compute tasks (Docker,…