Senior Simulation Data Engineer

Physicsx · Londonsenior

In short

▸Engineero de datos avanzado que gestiona flujos de simulaciones CFD a gran escala en la nube, integrando física, IA y Big Data.
▸Días típicos: orquestar simulaciones, construir pipelines de datos de alta velocidad y asegurar calidad e integridad en cada etapa.
▸Destacado: trabajo directo con científicos de investigación y equipos de IA en un entorno de hardware/software avanzado, con enfoque en física numérica y Fórmul

Fluency in English required for collaboration with international teams and documentation.

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What they ask for

✓Experiencia en orquestación de simulaciones CFD a gran escala en entornos HPC/cloud
✓Construcción y mantenimiento de pipelines de datos de alta performance para grandes volúmenes de datos
✓Conocimiento profundo en validación de datos, calidad de simulación y trazabilidad (provenance)
✓Capacidad para diseñar sistemas de monitoreo, alertas y manejo de fallos en flujos largos
✓Colaboración estrecha con científicos, ingenieros de IA y equipos de infraestructura
✓Experiencia en preprocesamiento de geometrías (mallas, morfología, validación de hermeticidad)

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CFD simulationsCloud computeJob scheduling systemsMonitoring and alertingData pipelinesGeometry preprocessingMesh preparationMorphingWatertightness validationPost-processing pipelines

Who should you write to at Physicsx?

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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. Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. The Role The Senior Simulation Data Engineer will extend and operate the infrastructure that powers our research Data Factory. You will be responsible for the end-to-end pipeline: from geometry preparation and simulation orchestration through validation, post-processing, and delivery to downstream ML training systems, using PhysicsX platform orchestration services where synergies exist. This role sits at the intersection of HPC engineering and data engineering. You will orchestrate long-running CFD simulations at scale, build robust data pipelines, and ensure that every simulation we produce meets rigorous quality standards. Team Context In this role, you will be vertically embedded in Research , working daily with: • Research Scientists who define data requirements and quality standards • ML Engineers who consume Data Factory outputs for model training • ML Infrastructure Engineers who are accountable for downstream training infrastructure You will have end-to-end responsibilities over the Data Factory, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. What you will do Simulation Orchestration • Extend and operate the Data Factory infrastructure that orchestrates thousands of CFD simulations per day on cloud compute • Design and operate job scheduling systems that maximize throughput while handling failures gracefully • Build monitoring and alerting to detect simulation failures, convergence issues, and resource bottlenecks early Data Pipeline Engineering • Build high-performance data pipelines that move simulation outputs from solver results to ML-ready training data • Implement geometry preprocessing workflows (mesh preparation, morphing, watertightness validation) • Design and operate post-processing pipelines: surface decimation, field interpolation, format conversion • Optimize I/O performance for large mesh datasets Data Quality and Validation • Implement comprehensive validation checks at every pipeline stage: solver convergence, physical field bounds, post-processing fidelity • Build systems that capture and quarantine bad data before they reach training pipelines • Track and report data quality metrics across the entire Data Factory • Work towards full provenance: training samples should be traceable back to their source geometry and simulation configuration Integration and Delivery • Deliver validated datasets to downstream ML training infrastructure in formats optimized for efficient data loading • Design data versioning and cataloging systems that support reproducible training runs • Work closely with ML Infrastructure Engineers to en

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