SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
PhysicsX is a deep-tech company building an AI-driven simulation software stack for engineering and manufacturing across advanced industries including Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. The company accelerates hardware innovation by enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle.
As a Machine Learning Software Engineer in Research, you will work closely with research scientists and simulation engineers to build and deliver models addressing real-world physics and engineering problems. Your responsibilities include designing, building, and optimizing machine learning models with a focus on scalability and efficiency; transforming prototype implementations into robust, optimized solutions; and implementing distributed training architectures for multi-node/multi-GPU training on cloud platforms (AWS, Azure, GCP) and on-premise services.
You will collaborate on designing and scaling foundation models for science and engineering, helping to optimize model training across large datasets and multi-GPU compute environments. You'll identify the best libraries, frameworks, and tools for modeling efforts, own research work-streams appropriate to your seniority level, and discuss results and implications with colleagues and customers. A key aspect of the role involves working at the intersection of data science and software engineering to translate research results into reusable libraries, tooling, and products. You will also mentor colleagues with less ML/engineering experience.
Required qualifications include an MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or related field, with demonstrated experience in scientific computing, high-performance computing (CPU/GPU clusters), or parallelized/distributed training for large/foundation models. Ideally you have 2+ years of professional experience in a data-driven role with exposure to scaling and optimizing ML models, distributed computing frameworks (Spark, Dask), high-performance computing frameworks (MPI, OpenMP, CUDA, Triton), cloud computing platforms, building ML models in Python (NumPy, SciPy, Pandas, PyTorch, JAX), C/C++ for computer vision or scientific computing, software engineering best practices, containerization/orchestration (Docker, Kubernetes, Slurm), and systematic experiment pipelines.
