3 months ago
Responsibilities
- Extend and operate Data Factory infrastructure orchestrating thousands of CFD simulations per day on cloud compute
- Design and operate job scheduling systems that maximize throughput and handle failures gracefully
- Build monitoring and alerting for simulation failures, convergence issues, and resource bottlenecks
- Build high-performance pipelines from solver results to ML-ready training data
- Implement geometry preprocessing workflows including mesh preparation, morphing, and watertightness validation
- Design and operate post-processing pipelines for surface decimation, field interpolation, and format conversion
- Optimize I/O performance for large mesh datasets
- Implement validation checks for solver convergence, physical field bounds, and post-processing fidelity
- Quarantine bad data before it reaches training pipelines and track data quality metrics
- Establish provenance linking training samples to source geometry and simulation configuration
- Deliver validated datasets to downstream ML training infrastructure in efficient loading formats
- Design data versioning and cataloging systems for reproducible training runs
- Work with ML Infrastructure Engineers on handoff between data production and model training
- Support multi-dataset training workflows
Requirements
- 5+ years of experience in data engineering, HPC engineering, or simulation infrastructure
- Strong experience with SLURM, Kubernetes, and Temporal
- Production experience building and operating reliable large-volume data pipelines
- Proficiency in Python for pipeline development and automation
- Systems engineering fundamentals covering Linux, networking, storage systems, and performance debugging
- Experience with cloud infrastructure, ideally CoreWeave or a similar GPU/HPC-focused cloud
- HPC experience in simulation engineering, including CFD, FEA, or similar computational workflows
- Experience with StarCCM+, OpenFOAM, ANSYS, or similar simulation tools
- Experience with geometry processing, mesh manipulation, CAD formats, and PyVista
- Familiarity with HDF5, VTK, NetCDF, and Zarr
- Experience with data quality engineering, validation frameworks, anomaly detection, and data observability
- Ability to scope and deliver projects while prioritizing activity
- Strong problem-solving, analytical, collaboration, and communication skills, particularly in a research setting
- Understanding of CFD fundamentals and solver outputs is preferred
- Experience with 3D geometry pipelines, including mesh decimation and field interpolation, is preferred
- Familiarity with ML data-loading patterns and training-system data consumption is preferred
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 scheme
Tech Stack
Categories
Data EngineeringDevOps
About PhysicsX
PhysicsX is a physical AI company on a mission to accelerate innovation and overhaul what engineering and manufacturing look like today. We are building a new software stack to deliver deep AI enablement across the entire engineering lifecycle. PhysicsX partners with leading organizations in aerospace & defense, automotive, semiconductors, materials, and energy, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London and New York. We are currently recruiting for multiple positions, however, please only apply for the role that best aligns with your skillset and career goals.