AI Engineer (US)
About The Position
Nexus Black builds AI to solve hard problems for the industries that keep the world turning: up on power lines, down on factory floors, and out in the field. Our products: Resolve (field service and predictive maintenance), Flow (production planning), Comply (regulatory compliance) and Reason (analytical reasoning) are used by technicians and engineers who cannot afford a tool that doesn't work first time. We pair 40 years of IFS industrial depth with the technical pace of a start-up, and we get results in weeks, not months. You'll build the agentic systems behind our products: agents that reason and orchestrate, deterministic code that calculates, and every output traceable. You'll ship production code from week one, and then watch a field technician use it on a customer site, because that's where our engineering standard is set.
Requirements
- Production experience with LLMs — you've shipped systems using model APIs (Anthropic, OpenAI or similar), not just notebooks: system-level prompt design, agent architectures, tool use, RAG.
- An evaluation habit. You've built eval pipelines — golden datasets, regression suites, LLM-as-judge where it's honest — and you can tell a good demo from a system that holds up in production.
- Strong software fundamentals: Python and/or TypeScript, Git, testing, CI, and the judgement to know when deterministic code beats a model call.
- Comfort with ambiguity and pace — small team, broad remit, shipping over ceremony.
- Curiosity about the physical world. Our users fix aircraft and keep grids running; you should want to understand their job well enough to build for it.
Nice To Haves
- Retrieval infrastructure at scale (vector stores, hybrid search, reranking).
- Fine-tuning or model-adaptation experience where it genuinely beat prompting.
- Experience in industrial, field-service or other asset-heavy domains.
- Open-source contributions or a portfolio of shipped AI systems.
Responsibilities
- Design and build agentic workflows — multi-step tool use, retrieval, and orchestration — using Claude, LangChain/LangGraph and whatever earns its place next.
- Own evaluation. Define what "correct" means for a workflow, build eval harnesses and golden datasets, and hold the line: if it wouldn't convince a 30-year veteran, it doesn't ship.
- Take features from prototype to production: observability, tracing (LangSmith), error handling, and reliability that survives week six, not just the demo.
- Join customer ride-alongs and site visits — plants, hangars, the field — and bring what you learn straight back into the code.
- Work with coding agents daily (Claude Code, Cursor) — they're how we work, not a novelty — and keep making your own job smaller.
- Contribute like an owner: PR reviews, design docs, and a culture of writing things down.
Benefits
- We're a small team building AI that has to work the first time, because the people using it are forty feet up a utility pole.
- AI tooling isn't a side experiment here — it's how we work every day.
- If you want your code in the hands of people who keep the world running, we'd like to talk.
- Less talk. More impact.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
