5 months ago
London, United KingdomStaff+
Responsibilities
- Shape Research group strategy and culture, particularly around scaled engineering, compute security, and infrastructure.
- Define the profiles and working patterns needed to execute the Research strategy and support team development.
- Own high-level Research workstreams, align priorities with internal and external stakeholders, and set technical direction.
- Plan roadmaps with clear milestones and guide junior team members in delivering against them.
- Work with research scientists and simulation engineers to build models for real-world physics and engineering problems.
- Design, build, and optimize scalable and efficient machine learning models.
- Transform prototype model implementations into robust, optimized implementations.
- Implement distributed multi-node and multi-GPU training architectures and explore federated learning using cloud and on-premise services.
- Help design, build, scale, and optimize foundation-model training for science and engineering.
- Select libraries, frameworks, and tools for modeling efforts.
- Discuss results and implications with colleagues and customers and connect them to real-world problems.
- Translate Research results into reusable libraries, tooling, and products.
- Mentor colleagues with less experience in machine learning and engineering.
Requirements
- MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field.
- At least 4 years of experience in a data-driven role in a professional industry setting.
- Experience scaling and optimizing machine learning models and training or serving foundation models at scale; federated learning is a bonus.
- Experience with distributed computing and high-performance computing frameworks such as Spark, Dask, MPI, OpenMP, CUDA, and Triton.
- Experience using hyperscaler cloud platforms such as AWS, Azure, or GCP.
- Experience building machine learning models and pipelines in Python using libraries and frameworks such as NumPy, SciPy, Pandas, PyTorch, and JAX, especially for deep learning.
- Experience with C/C++ for computer vision, geometry processing, or scientific computing.
- Experience with software engineering concepts and best practices, including versioning, testing, CI/CD, API design, and MLOps.
- Experience containerizing and orchestrating compute tasks with Docker, Kubernetes, or Slurm.
- Experience writing pipelines and experiment environments and running experiments systematically.
- Strong problem-solving, analytical, autonomous delivery, collaboration, and communication skills.
- Experience with scientific computing, CPU/GPU clusters, or parallelized and distributed training for large or foundation models.
Benefits
- Hybrid work model with time in the Shoreditch office and work-from-home days
- Equity options
- 10% employer pension contribution
- Free office lunches
- Enhanced parental leave, including 3 months full-pay paternity leave and 6 months full-pay maternity leave
- YellowNest nursery scheme
- 25 days of annual leave plus public holidays
- Private medical insurance with 100% employee cover
- Wellhub subscription
- Eye tests
- Personal development support
- Employee Assistance Programme
- Bike2Work scheme
- Season ticket loan
- Octopus EV salary sacrifice
Tech Stack
Categories
About PhysicsX
PhysicsX builds an AI-driven simulation software stack that enables high-fidelity, multi-physics modeling and optimization for engineering and manufacturing teams. The company sells software and delivery services to enterprises in aerospace and defense, automotive, semiconductors, materials, and energy. It is privately held and headquartered in London, with offices in London and New York.