Founding Member of Technical Staff ML Systems

Everything humans build is driven by simulation: the shape of a turbine blade, the structureof a chassis, the cooling path through a battery pack. And simulation is still painfully slow:millions of engineers wait hours or days for a single run, forcing them to explore just a fewdesigns when the best one may lie in the thousands they never reach. Replacing simulation with direct prediction is one of the great unsolved problems in AI.

Our answer is a foundation model for hardware engineering: a large-scale physics AI model thatturns simulation into inference, collapsing hours into seconds. One model across the full lifecycle ofa physical product, from design through engineering to manufacturing. The ambition is simple:become the AI infrastructure underneath how the world builds hardware.

We're a tight-knit team in Zurich, with engineers from Caltech, Google, CERN, and ETH working alongside a professor from Cambridge. Ideas go from whiteboard to training run in a day, and everyone touches everything.

We are hiring a Founding Member of Technical Staff to own problems end to end:
From a researchidea, to training a model reliably at scale, to a system we can deploy and trust. This is asenior role with substantial scope over our technical direction. You willwork across the full stack of an ML system, architecture, data, training infrastructure,deployment; your decisions will drive the evolution of the product from day one.

• Model development: Design and train models for 3D solid mechanics andfluid dynamics, and run the experiments that move the models forward.
• Research: Track the literature, prototype quickly and judge which ideasare worth taking from paper to production.
• Distributed training: Build and optimize distributed training in PyTorch,multi-node DDP, high-throughput data loading, reproducible large-scale runs and debug itwhen it breaks.
• Data infrastructure: Own the pipeline from raw simulation output to efficient, streamed training data at scale.
• Productionization: Turn models into reliable, observable services thatengineers outside the team can depend on.

• Seniority: You have spent several years building ML systems in a seriousenvironment, big tech, a leading research institution, or a comparably demanding setting.
  
  This is not an entry-level role; we are looking for people with a substantial track recordof shipping real systems.
• Raw talent: We care a great deal about how you think. Evidence of exceptional talent, strong performance in math or computer science, competitiveprogramming, research with real results, or anything else that demonstrates it, is genuinely valuable to us.
• Engineering: Strong software engineering fundamentals and a history of building and shipping production ML systems. You write code that a team can build on, andyou care about correctness and reliability.
• ML experience: Proficiency in Python and PyTorch, and hands-on experience training models at scale, including the practical realities of distributed training,throughput and large-scale data pipelines.
• Research ability: You can read a paper, assess whether it is worthimplementing and design a clean experiment to test it.
• Optional Domain knowledge: Familiarity with physics, numerical methods, or scientific computing, FEM, CFD, PDEs, gives you a headstart and we weight it heavily. But the modelling and systems challenges are the core ofthe job and we have strong engineers who learned the physics here.

We take on hard technical problems and hold ourselves to a high bar. The team is committed, ownership is real, and the pace is demanding. We are direct about thisbecause it is central to how we operate.

We are looking for people with a demonstrated history of exceptional drive:
Pushing a hardproblem to a result others thought was out of reach. In your application, point to concreteevidence of that from your own past work.

• The technical challenge: Building foundation models for physics is an unsolvedproblem with real industrial impact, not a thin wrapper around someone else's model.
• Real ownership: Founding role with meaningful equity, significantinfluence over the technical direction, and a real say in where the company goes.
• The team: Highly focused group of engineers and researchers from ETH, Caltech,Cambridge and Google who back each other.
• Compensation: Competitive salary and significant equity, because we hirepeople who could work anywhere and we want that to be an easy part of the decision.