Modeling and Simulation Engineer, Thermofluid mechanics

Atomic Machines•Emeryville, CA

About The Position

Atomic Machines is ushering in a new era of micromanufacturing with its Matter Compiler™ technology platform. This platform enables new classes of micromachines to be designed and built by providing manufacturing processes and a materials library that are inaccessible to semiconductor manufacturing methods. It unlocks MEMS manufacturing not only for device classes that could never be produced by semiconductor methods, but also for entirely new categories. Furthermore, this digital platform is fully programmable in the way 3D printing is digital—but whereas 3D printing produces parts of a single material using a single process, the Matter Compiler™ technology platform is a multi-process, multi-material system: bits and raw materials go in, and complete, functional micromachines come out. The Atomic Machines team has also created an exciting first device—made possible only through the Matter Compiler™ technology platform—that we will be unveiling to the world soon. Our offices are in Emeryville and Santa Clara, California. We are seeking an L4 Modeling and Simulation Engineer to join our growing AI and Modeling & Simulation team. The ideal candidate has experience developing and deploying mathematical and numerical models across multiple coupled physics domains. In this role, you will identify the critical attributes of existing manufacturing systems and future machine concepts, then use those insights to build, simulate, and deploy parameterized models that support machine design and optimization. To succeed in this role, you should have experience with multiscale and multiphysics modeling and be comfortable using simulation tools such as COMSOL, Ansys, or similar platforms. You should also have a strong foundation in structural mechanics, heat transfer, and CFD. Familiarity with other simulation domains, such as electromagnetics or electrochemistry, is a plus. This is an excellent opportunity for someone with a track record of building high-quality, validated multiphysics models and who is excited to apply those skills to support the development of new classes of devices manufactured at scale on Atomic’s platform. The ideal candidate thrives in a collaborative environment and is effective at documenting, communicating, and presenting technical work.

Requirements

4+ years of hands-on experience in multiphysics modeling and simulation following completion of a bachelor’s degree (or recent PhD).
Strong foundation in multiphysics simulation, with particular depth in structural mechanics, heat transfer, and fluid dynamics.
Programming experience, especially in Python, and experience automating simulation workflows for design and optimization.
Experience with COMSOL and/or Ansys; experience with Abaqus, Siemens tools, or related software is a plus.
Strong written and verbal communication skills, with the ability to work independently and collaboratively.
Resourceful, flexible, and adaptable approach to technical work.
Bachelor’s, master’s, or PhD in engineering, physics, or a related field.

Nice To Haves

Experience with CAD tools such as Onshape, Fusion 360, SolidWorks, NX, or similar.
Experience with data analysis and visualization tools such as JMP, MATLAB, pandas, or matplotlib.
Industry experience with product development processes such as Agile or similar frameworks.

Responsibilities

Identify critical system attributes and develop computational models for current and future manufacturing machines, with an emphasis on structural, thermal, and fluid systems.
Define figures of merit, boundary conditions, and operating conditions based on system requirements and cross-functional input.
Build and deploy multiphysics simulations, including thermal, mechanical, and fluid models, using finite element and/or finite volume methods for both steady-state and transient analyses.
Deliver executable computational models that support sensitivity analysis, design exploration, and optimization.
Propose validation experiments and use experimental results to calibrate, validate, and improve models.
Document simulation methods, assumptions, and validation results, and communicate findings clearly to the broader team.
Collaborate cross-functionally with prototype engineers, designers, and AI engineers.