PhysicsX is a deep-tech company focused on accelerating hardware innovation through AI-driven simulation software for engineering and manufacturing. The Machine Learning Software Engineer will collaborate with research scientists and simulation engineers to develop machine learning models that solve real-world physics and engineering challenges, while optimizing model training and implementation.
Responsibilities
- 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
- 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 a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor
Skills
- 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 >1 years of experience in a data-driven role, 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
Benefits
- Equity options – share in our success and growth.
- 5% 401(k) match – invest in your future.
- Flexible working – balance your work and life in a way that works for you.
- Hybrid setup – enjoy our Manhattan office while keeping remote flexibility.
- Enhanced parental leave – support for life’s biggest milestones.
- Private healthcare – comprehensive coverage for you and your family.
- Personal development – access learning and training to help you grow.
- Work from anywhere – extend your remote setup to enjoy the sun or reconnect with loved ones.
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