Backed by Sequoia.
About the role
You will work on the AI and LLM engineering layer that connects Haven’s evidence base and knowledge graph to the product experiences investigators and safety leaders use. This is a build-and-operate role reporting to the CTO. You will design the reasoning, ship it, instrument it, and improve it using production evidence. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Production LLM systems. AI or ML engineering, including shipping LLM systems that real users depend on
- Agentic workflows. Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework
- Evaluation discipline. A repeatable approach to LLM evaluation, including representative datasets, regression testing, LLM-as-judge techniques, or human review loops
- Retrieval judgment. Experience assembling context for LLMs and a clear point of view on what to retrieve, how much, and why
- Production ownership. A track record of owning systems from deployment through monitoring and incident response, including a strong story about a failure or regression you diagnosed and fixed
- Model judgment. Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk
More about this role
Haven Safety AI is building the enterprise learning intelligence layer for safety. Co-founded with The AES Corporation and AI Fund, the venture studio founded by Andrew Ng, Haven helps high-risk organizations learn faster from what goes wrong so they can prevent what comes next.
Haven works alongside existing EHS enterprise systems to improve how organizations investigate, assess, and learn from incidents. INVESTIGATE guides evidence synthesis, timeline development, multi-threaded causal analysis, and corrective actions. ASSURE continuously reviews completed investigations for evidence quality, causal coverage, guideline adherence, and CAPA strength. LEARN reasons across incident history to surface recurring control failures, repeated corrective-action patterns, CAPA debt, and emerging signals.
The platform combines current incident evidence, company knowledge, historical cases, and an industry knowledge graph. A coordinated team of specialized AI agents examines evidence, controls, engineering factors, procedures, regulations, training, and organizational history, then produces one traceable assessment for human review. Customers have reported an 80% reduction in root cause...
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