Everstar Inc.

Founding AI Engineer

New York City · Exec

Sponsorship not specifiedDetected 208 days ago

Full-Stack DevelopmentGitVector DatabasesMachine LearningLLMsRAGAgentic AIMLOpsAI OrchestrationThermal AnalysisResearchLeadershipCollaboration

About the role

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.
Not a researcher.

you're early enough to shape how we think about model selection, prompt design, guardrails, physics-AI integration, and the entire ML ops stack
3-8 years building production ML/LLM systems-RAG, fine-tuning, evals, agent orchestration. You've shipped models that users depend on daily.
Strong founding AI engineers typically grow into Head of AI/ML, AI Research Lead, or CTO-track roles as the company scales.
TL;DR: Build AI that accelerates nuclear deployment.
Own AI production from evals to fine-tuning.
Everstar builds the intelligence layer that makes nuclear power actually deployable-collapsing regulatory and manufacturing timelines from years to months.
You'll build alongside engineers from Tesla, SpaceX, Lockheed Martin, Google, and Microsoft.
Physics-informed design safety analyses using world models that reason about thermal hydraulics, neutronics, and structural integrity
Build shit that matters, accelerating nuclear energy and shaping the AI future

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)
Background in physics, engineering, or computational science
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.

Hugging Face, LangChain, vector databases, prompt engineering, and modern LLM ops.
Shipped ≥3 major model improvements to production (better evals, new fine-tuned model, or agent capability).
Inference latency reduced ≥30% or accuracy improved ≥15% on key benchmarks.

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
Top of market base + meaningful equity in a fast-growing company; standard benefits (health/dental/vision, FSA, wellness stipend).
New York City (5 days on-site) · Top of market + equity + benefits

create benchmarking suites that catch regressions before customers do; instrument quality metrics that actually matter.

This listing is sourced directly from Everstar Inc.'s careers page and normalized into a canonical job model.