Role overview
*Senior MLOps / ML Infrastructure Engineer
About the Company**
Our client is a Series B, venture-backed deep-tech company building a Physics AI platform that helps engineering teams bring products to market faster, reduce development risk, and explore better designs with greater confidence. The platform combines large-scale simulation data with modern machine learning to generate high-fidelity predictions of physical behavior in near real time. Customers include leading organizations across aerospace, automotive, and advanced manufacturing, working on some of the most demanding real-world engineering problems.
The Role
This role focuses on building and operating the infrastructure that powers physics-based AI systems at scale. The position enables ML engineers and scientists to train, track, deploy, and monitor models reliably without managing low-level infrastructure. The work sits at the intersection of ML systems, cloud infrastructure, and large-scale simulation data, with a strong emphasis on performance, reliability, and developer productivity. It is a hands-on engineering role in a fast-moving, in-office environment, working closely with ML researchers, platform engineers, and product teams.
What we're looking for
- Interest in physics simulation, scientific computing, or HPC environments
- Experience building production MLOps platforms in deep-tech or simulation-heavy environments
- Familiarity with additional programming languages such as Go or C++
This role suits someone who enjoys startup environments, learns quickly, and communicates clearly across disciplines. The team works on-site five days a week and values close collaboration, fast feedback loops, and hands-on problem solving. There is a strong belief that great infrastructure should be largely invisible, enabling engineers and scientists to move faster without friction.