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AI Fund
AI Fund
11 – 50 Employees
ConsultingHealthcareManufacturing
AI Fund is a venture studio that helps launch and develop companies built around artificial intelligence. Working with founders and technology pioneers, it takes a minority co-founder role and supports the creation of AI applications across fields including manufacturing, mental health, education, and healthcare. Its work combines venture building with practical support for teams applying AI to challenges in diverse industries.
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Software Engineer, AI Systems (US Remote)

Build production LLM agents for Haven Safety’s enterprise safety intelligence platform. Own retrieval, evaluation, observability, and reliability systems for safety-critical investigations.

USUnited StatesRemoteFull-timeMiddleSeniorEnglish RequiredAI EngineerMachine Learning Engineer
AzureElasticSearchMongoDBNeo4jPython

Description

  • Develop and run multi-step LLM pipelines that coordinate model and tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
  • Expand Haven’s coordinated agent team and orchestration layer from incident evidence through analysis, review, and enterprise learning.
  • Create the context layer using Neo4j traversal, vector search, and hybrid retrieval.
  • Develop evaluation datasets, scoring methods, regression suites, model comparisons, human-review loops, and stage-level quality attribution.
  • Add tracing, tool-call auditing, cost and latency monitoring, failure recovery, and quality dashboards.
  • Identify agent loops, hallucinations, and silent performance drift before they affect customers.
  • Choose among OpenAI, Anthropic, and Google models according to task requirements.
  • Work closely with product and knowledge engineering teams.
  • Contribute to the direction of the AI roadmap.
  • Design, deliver, instrument, and continuously improve AI and LLM reasoning systems using production evidence.
  • Report directly to the CTO.

Requirements

  • Experience in AI or machine learning engineering, including delivering LLM systems used by real customers.
  • Practical experience building and troubleshooting multi-step, tool-using workflows with LangGraph, LangChain, or a comparable framework.
  • A systematic approach to LLM evaluation using representative datasets, regression tests, LLM-as-judge methods, or human-review loops.
  • Experience preparing context for LLMs, with a clear rationale for what to retrieve, how much to retrieve, and why.
  • Ownership of production systems through deployment, monitoring, and incident response, including diagnosing and resolving failures or regressions.
  • Ability to work across model providers and explain tradeoffs involving quality, latency, cost, context, and operational risk.
  • Strongly preferred: experience with Neo4j and Cypher or a comparable graph database, plus the ability to learn graph data modeling quickly.
  • Advanced Python skills.
  • Production experience with FastAPI, asynchronous services, testing, observability, and maintainable interfaces.
  • Nice to have: Cypher expertise, schema evolution, MERGE patterns, embeddings, and operation of a live knowledge graph.
  • Nice to have: awareness of prompt injection, context leakage prevention, tenant isolation, role-based access, and policy-layer separation.
  • Nice to have: experience operating production AI services in Azure and working with Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or a similar platform.
  • Strongly preferred: previous experience working in an enterprise environment.
  • Applicants must be based in the United States; relocation is not considered.

Benefits

  • Work on substantial AI reasoning challenges.
  • Join a team that prioritizes evaluation in its engineering practice.
  • Deliver technology with a direct, visible effect on customers.
  • Take on broad ownership within a small team.
  • Receive direct input from safety teams and end users.
  • Work closely with the CTO, product, and knowledge engineering teams.
  • Make consequential technical decisions and see results reach customers quickly.

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