Senior Applied Engineer

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. PhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative. What you will do Work closely with our research scientists, simulation engineers, customers and partners to deliver AI models that address real-world physics and engineering problems. Design and build physical foundation models with a focus on efficiently scaling model training to large data on multi-GPU cloud compute. Transform prototypes from your research scientist colleagues into robust and optimised implementations, challenging architecture decisions that hurt scalability. Identify and argue for the best libraries, frameworks and tools 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 curiosity and initiative among your colleagues and mentees. 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. Ideally, >2 years of experience in a data-driven, 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., NumPy, SciPy, 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. What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collab…