<div class="content-intro"><h2>About us</h2> <div>PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.</div> <div>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.</div></div><h3><strong>Note: </strong>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.</h3> <h2><strong>What you will do </strong></h2> <ul> <li>Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems.</li> <li>Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain.</li> <li>Transform prototype model implementations to robust and optimised implementations.</li> <li>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.</li> <li>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.</li> <li>Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.</li> <li>Own Research work-streams at different levels, depending on seniority.</li> <li>Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.</li> <li>Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.</li> <li>Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.</li> </ul> <h2><strong>What you bring to the table</strong></h2> <ul> <li>Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.&