Staff Machine Learning Software Engineer, Research

About us PhysicsX 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, PhysicsX 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. What you will do  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 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.   What you bring to the table 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., NumPy, SciPy, 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 be