Everstar develops AI-powered regulatory compliance solutions that accelerate licensing and operations for nuclear energy and other industrial projects.
FOUNDING AI ENGINEER (AI + PRODUCTION) New York City (5 days on-site) · Top of market + equity + benefits TL;DR: Build AI that accelerates nuclear deployment. Own AI production from evals to fine-tuning. Push the frontier on physics models, world models, and AI-accelerated simulations. High-leverage IC role with founding-level impact. THE MISSION Everstar builds the intelligence layer that makes nuclear power actually deployable—collapsing regulatory and manufacturing timelines from years to months. Gordian already powers engineering and compliance work for utilities, advanced reactor companies, and hyperscalers. We pair deep nuclear domain expertise with frontier AI and move with startup speed. Now we need a Founding AI Engineer to turn research breakthroughs into production systems that ship—and push beyond LLMs into physics-informed AI, world models, and simulation acceleration. You’ll be joining the Apollo Team of Nuclear. You’ll build alongside engineers from Tesla, SpaceX, Lockheed Martin, Google, and Microsoft. You’ll learn from nuclear and national security experts who cut their teeth at the Nuclear Regulatory Commission, CIA, and NuScale. THE ROLE (REPORTING TO THE CEO) Not a researcher. Not a prompt engineer. This is a production-first role. You'll own the AI stack end-to-end—from eval frameworks to fine-tuning pipelines to agent orchestration. But you'll also push the boundaries of what AI can do for nuclear: AI-accelerated weather simulations, design safety analyses powered by physics models, and world model applications that transform nuclear operations. Think 70% building production systems / 30% frontier R&D with access to Microsoft and NVIDIA's latest tools through our first-party partnerships and a large AI research budget to experiment aggressively. Most weeks you'll be shipping new model capabilities, debugging eval failures, and scaling inference—then immediately applying what you learned to the next sprint. Some weeks you'll be prototyping physics-informed models, running GPU-accelerated simulations, or collaborating directly with NVIDIA and Microsoft researchers. You will: - Build production AI agents: power Gordian Search, Research, and Compose with outputs that are truthful, complete, and auditable—because in nuclear, "mostly right" isn't good enough. - Design eval infrastructure: create benchmarking suites that catch regressions before customers do; instrument quality metrics that actually matter. - Own fine-tuning pipelines: generate synthetic data, run ablations, and ship domain-adapted models that outperform off-the-shelf LLMs on nuclear regulatory tasks. - Push the frontier (R&D): - AI-accelerated weather simulations for site qualification and environmental impact assessments—replacing months of modeling with hours - Physics-informed design safety analyses using world models that reason about thermal hydraulics, neutronics, and structural integrity - Vision + physics models for automated document analysis, construction monitoring, and operational anomaly detection - Agentic workflows that compound over time, learning from each regulatory submission to improve the next - Leverage NVIDIA partnership: work directly with NVIDIA's research team to access cutting-edge tools (NeMo, Modulus, Omniverse) and contribute to the future of AI for critical infrastructure - Set technical direction: you're early enough to shape how we think about model selection, prompt design, guardrails, physics-AI integration, and the entire ML ops stack - Mentor and lead: as the team scales, you'll hire and guide other AI engineers—but first, you'll prove the playbook yourself A sample week: debug why Research citations dropped 8%; ship new fine-tuned model for compliance drafting; design eval suite for multi-document reasoning; prototype physics-informed model for thermal analysis; pair with fullstack engineer to optimize inference latency; attend NVIDIA collaboration session on world models; read three ML papers and implement one idea. WHAT YOU'VE DONE - 3–8 years building production ML/LLM systems—RAG, fine-tuning, evals, agent orchestration. You've shipped models that users depend on daily. - Mastery of the stack: Hugging Face, LangChain, vector databases, prompt engineering, and modern LLM ops. You know when to use off-the-shelf and when to build custom. - Rigor with evals: you've designed benchmark suites, tracked model quality over time, and know how to measure what matters (not just what's easy). - Leadership DNA: you've owned outcomes, not just tasks. You've set technical direction, mentored teammates, or led cross-functional projects. - Bonus points: - Experience with physics-informed neural networks, scientific computing, or simulation acceleration - Published research in ML/AI, contributions to open-source ML frameworks - Deep familiarity with NVIDIA tools (NeMo, Modulus, CUDA optimization) - You're the person who reads Arxiv papers on weekends and immediately wants to implement them - 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. WHO YOU'RE BUILDING FOR 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 ne