Simulation Engineer 
Location: Onsite - Bay Area.
Company: High-growth AI startup (stealth / early-stage)
Focus: Physics-based simulation to ML-driven systems
Overview
Our 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 Hiring
Candidates 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 / UAVs
  • Aviation (commercial or defence aircraft systems)
  • Space / Rocket Systems
What You’ll Do
  • Develop and apply high-fidelity simulation models across fluid, structural, thermal, biological, or aerodynamic systems
  • Translate simulation outputs into ML-compatible datasets and representations
  • Work closely with ML and AI teams to enable surrogate modelling, optimisation, and system-level learning
  • Improve simulation performance, scalability, and reliability across large-scale compute environments
  • Design end-to-end pipelines from simulation through to data generation, model training, and deployment
  • Validate and calibrate models against real-world data where available
What They’re Looking For
Core Requirements:
  • Strong background in simulation engineering within a real-world domain
  • Experience with tools such as OpenFOAM, ANSYS Fluent, STAR-CCM , Abaqus, ANSYS Mechanical, COMSOL
  • Experience 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 environments
Preferred:
  • Exposure to ML workflows (PyTorch, TensorFlow, surrogate models, optimisation loops)
  • Experience generating or working with synthetic data from simulations
  • Familiarity with distributed compute, GPU acceleration, or cloud-based simulation pipelines
  • Background in companies such as:
    • Medical Devices: Stryker, Medtronic, Boston Scientific, Zimmer Biomet
    • Drones/UAVs: Skydio, DJI, Autel, Parrot
    • Aerospace/Aviation: Boeing, Airbus, Joby, defence organisations
    • Space: SpaceX, Relativity Space, NASA, Project Kuiper, Muon Space
What Makes This Different
  • You are helping turn simulation into intelligence, not just running models
  • Direct exposure to next-generation AI systems grounded in physics
  • Opportunity to work across multiple industries and problem domains
  • High ownership in shaping how simulation integrates into AI systems for the physical world
Ideal Profile
  • Domain expert first, not a generalist
  • Has built simulations that informed real-world decisions
  • Comfortable operating in ambiguous, early-stage environments
  • Interested in bridging physics and machine learning
Hiring Priority
  • Bioreactors / Bio-simulation (urgent)
  • CFD / Fluid systems
  • Aerospace / UAV
  • Aviation
  • Space systems