Simulation Engineer Location: Onsite - Bay Area.Company: High-growth AI startup (stealth / early-stage)Focus: Physics-based simulation to ML-driven systemsOverviewOur client is building a new class of AI systems designed to understand and operate within real-world physical environments. The company sits at the intersection of simulation, machine learning, and industrial systems, with a focus on turning high-fidelity simulation data into scalable, production-grade intelligence.They are hiring Simulation Engineers across multiple domains who can bring deep subject-matter expertise and translate complex physical systems into computational models that can be learned, optimised, and deployed. This is not a pure research role. It is for engineers who have built and used simulation systems in real-world environments and understand how those systems behave under production constraints.Key Areas of HiringCandidates should come from one of the following domains:Bioreactors / Bioengineering (top priority)CFD / Fluid Dynamics (medical devices or industrial systems)Aerospace (flight physics, aerodynamics, control systems)Fixed-Wing Drones / UAVsAviation (commercial or defence aircraft systems)Space / Rocket SystemsWhat You’ll DoDevelop and apply high-fidelity simulation models across fluid, structural, thermal, biological, or aerodynamic systemsTranslate simulation outputs into ML-compatible datasets and representationsWork closely with ML and AI teams to enable surrogate modelling, optimisation, and system-level learningImprove simulation performance, scalability, and reliability across large-scale compute environmentsDesign end-to-end pipelines from simulation through to data generation, model training, and deploymentValidate and calibrate models against real-world data where availableWhat They’re Looking ForCore Requirements:Strong background in simulation engineering within a real-world domainExperience with tools such as OpenFOAM, ANSYS Fluent, STAR-CCM , Abaqus, ANSYS Mechanical, COMSOLExperience building or working with custom simulation frameworks (C , Python, MATLAB or similar)Solid understanding of physics-based modelling (fluids, thermodynamics, structural mechanics, control systems, or bio-systems)Experience working with large-scale simulations or HPC environmentsPreferred:Exposure to ML workflows (PyTorch, TensorFlow, surrogate models, optimisation loops)Experience generating or working with synthetic data from simulationsFamiliarity with distributed compute, GPU acceleration, or cloud-based simulation pipelinesBackground in companies such as:Medical Devices: Stryker, Medtronic, Boston Scientific, Zimmer BiometDrones/UAVs: Skydio, DJI, Autel, ParrotAerospace/Aviation: Boeing, Airbus, Joby, defence organisationsSpace: SpaceX, Relativity Space, NASA, Project Kuiper, Muon SpaceWhat Makes This DifferentYou are helping turn simulation into intelligence, not just running modelsDirect exposure to next-generation AI systems grounded in physicsOpportunity to work across multiple industries and problem domainsHigh ownership in shaping how simulation integrates into AI systems for the physical worldIdeal ProfileDomain expert first, not a generalistHas built simulations that informed real-world decisionsComfortable operating in ambiguous, early-stage environmentsInterested in bridging physics and machine learningHiring PriorityBioreactors / Bio-simulation (urgent)CFD / Fluid systemsAerospace / UAVAviationSpace systems
Sam Warwick
