Founding AI Engineer

Overview Founding AI Engineer (AI + Production) — New York City (on-site 5 days). Top of market base compensation, equity, and benefits. The Mission Everstar builds the intelligence layer that makes nuclear power deployable — collapsing regulatory and manufacturing timelines from years to months. Gordian powers engineering and compliance for utilities, advanced reactor companies, and hyperscalers. We combine deep nuclear domain expertise with frontier AI and startup speed. We are seeking a Founding AI Engineer to turn research breakthroughs into production systems that ship — pushing beyond LLMs into physics-informed AI, world models, and simulation acceleration. You’ll join the Apollo Team of Nuclear. You’ll work with engineers from Tesla, SpaceX, Lockheed Martin, Google, and Microsoft, and learn from nuclear and national security experts from organizations such as the Nuclear Regulatory Commission, CIA, and NuScale. The Role (reporting to the CEO) This is a production-first role. Not a researcher and not a prompt engineer. You will own the AI stack end-to-end — from eval frameworks to fine-tuning pipelines to agent orchestration. You will also push the boundaries of what AI can do for nuclear, including AI-accelerated weather simulations, physics-model-powered design safety analyses, and world model applications that transform nuclear operations. Think 70% building production systems / 30% frontier R&D, with access to Microsoft and NVIDIA tools through first-party partnerships and a large AI research budget to experiment aggressively. Typical weeks involve shipping new model capabilities, debugging eval failures, and scaling inference, then applying lessons to the next sprint. Some weeks involve prototyping physics-informed models, running GPU-accelerated simulations, or collaborating 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. Design eval infrastructure : create benchmarking suites that catch regressions before customers do; instrument quality metrics that 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—reducing modeling time from months to 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 learn from each regulatory submission to improve the next. Leverage NVIDIA partnership : collaborate with NVIDIA's research team to access cutting-edge tools (NeMo, Modulus, Omniverse) and contribute to AI for critical infrastructure. Set technical direction : shape model selection, prompt design, guardrails, physics-AI integration, and the ML ops stack. Mentor and lead : as the team scales, hire and guide other AI engineers — initially demonstrate the playbook yourself. A sample week: debug citation drops, ship a new fine-tuned model for compliance drafting, design an eval suite for multi-document reasoning, prototype a physics-informed model for thermal analysis, pair with a full-stack engineer to optimize latency, and participate in an NVIDIA collaboration session on world models; read three ML papers and implement one idea. The What You’ve Done 3–8 years building production ML/LLM systems (RAG, fine-tuning, evals, agent orchestration). You have 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 versus build custom. Rigor with evals : designed benchmark suites, tracked model quality over time, and know how to measure what matters. Leadership DNA : owned outcomes, 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 or contributions to open-source ML frameworks Deep familiarity with NVIDIA tools (NeMo, Modulus, CUDA optimization) You read Arxiv papers on weekends and implement ideas 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 role directly aids: Nuclear operators keeping 20% of U.S. electricity safe and reliable Advanced reactor developers navigating regulatory approval and using AI-accelerated simulations to optimize designs Licensing teams drafting safety analyses powered by physics models Site qualification teams running environmental and weather analyses Second-order effects: nuclear unlocks AI energy needs; AI accelerates deployment; and the tokens you generate translate into safer infrastructure and a livable planet. What’s at Stake If we succeed : We unlock nuclear at scale, power the AI revolution with clean energy, and shorten licensing timelines. Physics-informed AI becomes a standard for critical infrastructure. If we fail : Nuclear remains bottlenecked, AI energy demand outpaces clean supply, and deployment remains stalled. What Success Looks Like (90 days) Shipped ≥3 major model improvements to production Eval framework instrumented and running continuously; regressions caught before customers Inference latency reduced ≥30% or accuracy improved ≥15% on key benchmarks Prototype ≥1 frontier capability with clear customer value Set the technical roadmap for AI engineering At least one system built is now core infrastructure Resources at Your Disposal NVIDIA & Microsoft first-party partnership : direct access to research teams, early tool access, and collaboration Large AI research budget : aggressive compute allocation for training and frontier R&D Latest NVIDIA hardware : H100s, GH200s, and future architectures World-class team : nuclear domain experts and AI researchers Growth Path Founding AI engineers typically grow into Head of AI/ML, AI Research Lead, or CTO-track roles as the company scales. Frontier R&D opens paths toward Chief Scientist or VP of Applied Research. First, you'll prove you can own the entire LLM stack and ship production systems that matter. Why Everstar Work with the best : high-caliber team Build impactful work , accelerating nuclear energy and shaping the AI future Large AI research budget for compute and experimentation Top of market base + meaningful equity and benefits IRL in NYC (midtown/Bryant Park); occasional travel How to Apply (show, don't tell) Submit application with: Resume AND LinkedIn profile GitHub or portfolio: show us something you built 200 words: What excites you most about building AI for nuclear deployment? 150 words: Describe a production ML system you owned and the hardest technical tradeoffs Bonus: If you have physics-informed AI or simulation experience, share an example. We respond to strong submissions within one week. Let’s build. #J-18808-Ljbffr