Founding AI Engineer

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  Experience with physics-informed neural networks, scientific computing, or simulation acceleration

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  Published research in ML/AI, contributions to open-source ML frameworks

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  Deep familiarity with NVIDIA tools (NeMo, Modulus, CUDA optimization)

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  You're the person who reads Arxiv papers on weekends and immediately wants to implement them

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  Background in physics, engineering, or computational science

No nuclear background required—only the hunger to build AI that matters and push the boundaries of what AI can do for physical systems.

This isn't benchmarks for benchmarks' sake. Your models will directly help:

• Nuclear operators keeping 20% of U.S. electricity safe and reliable

• Advanced reactor developers navigating regulatory approval for next-gen designs—and using AI-accelerated simulations to optimize designs in days, not months

• Licensing teams drafting safety analyses that take months today, hours tomorrow—powered by physics models that understand first principles

• Site qualification teams running environmental and weather analyses that currently require expensive consultants and 6+ month timelines

And the second-order effects matter even more:

• Nuclear unlocks the energy needed for AGI/ASI—advanced AI requires unprecedented power.

• AI accelerates nuclear deployment—breaking the regulatory bottleneck that's held back clean energy for decades.

• The tokens you generate translate into safer infrastructure and a livable planet.

• 🔥 If we succeed: We unlock nuclear at scale, power the AI revolution with clean energy, and collapse licensing timelines from years to months. The models you build help humanity leap toward AGI on a sustainable foundation. Your physics-informed AI becomes the standard for how critical infrastructure is designed and operated.

• ❄️ If we fail: Nuclear stays bottlenecked in decades-old processes, AI's energy demand outpaces clean supply, and we miss the window to align technological progress with climate survival. The frontier AI capabilities remain academic curiosities instead of deployment accelerators.

• Shipped ≥3 major model improvements to production (better evals, new fine-tuned model, or agent capability).

• Eval framework is instrumented and running continuously; you catch quality regressions before customers report them.

• Inference latency reduced ≥30% or accuracy improved ≥15% on key benchmarks.

• Prototype ≥1 frontier capability (physics model for safety analysis, weather simulation acceleration, or world model application) that shows clear customer value.

• You've set the technical roadmap for AI engineering and the team trusts your judgment.

• At least one system you built (eval suite, fine-tuning pipeline, or agent orchestration) is now core infrastructure the company depends on.

• NVIDIA & Microsoft first-party partnership: Direct access to Microsoft & NVIDIA research team, early access to new tools (NeMo, Modulus, Omniverse), and collaboration on frontier AI applications

• Large AI research budget: Aggressive compute allocation for training runs, experiments, and frontier R&D—no need to beg for GPU credits

• Latest NVIDIA hardware: Access to H100s, GH200s, and future architectures as they become available

• World-class team: Work alongside nuclear domain experts, AI researchers, and engineers who've shipped at SpaceX and top startups

Strong founding AI engineers typically grow into Head of AI/ML, AI Research Lead, or CTO-track roles as the company scales. The frontier R&D component opens paths toward Chief Scientist or VP of Applied Research as we expand into physics-AI and world models.

First, you'll prove you can own the entire LLM stack and ship production systems that matter.

• Work with the best: high‑caliber, wartime team that builds things that scale

• Build shit that matters, accelerating nuclear energy and shaping the AI future

• Large AI research budget for compute, conferences, and experimentation.

• Top of market base + meaningful equity in a fast-growing company; standard benefits (health/dental/vision, FSA, wellness stipend).

• IRL in NYC (midtown/Bryant Park). Occasional travel to client sites, Microsoft & NVIDIA offices, or ML conferences.

Submit application with:

1. Resume AND LinkedIn profile

2. GitHub or portfolio: show us something you built (open-source contributions, side projects, or production work you're proud of)

3. 200 words: "What excites you most about building AI for nuclear deployment?"

4. 150 words: "Describe a production ML system you owned. What were the hardest technical tradeoffs and how did you resolve them?"

5. Bonus (optional): If you have experience with physics-informed AI, simulation acceleration, or scientific computing, share a brief example of work in this domain.

We respond to strong submissions within one week.

Let’s build.