About Haven Safety: 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. 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. 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 analysis labor time using Haven. 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. You will design the reasoning, ship it, instrument it, and improve it using production evidence. Haven serves enterprise customers in regulated, safety-critical industries through multi-tenant and dedicated deployments. Build and operate multi-step LLM pipelines that coordinate model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs. Extend Haven’s coordinated agent team and the orchestration layer that carries an incident from evidence through analysis, review, and enterprise learning. Build datasets, scoring, regression suites, model comparisons, human-label loops, and per-stage quality attribution for extraction and reasoning tasks. Production AI operations. Implement tracing, tool-call audits, cost and latency monitoring, failure handling, and quality dashboards; catch loops, hallucinations, and silent drift before customers do. Technical direction.



partner with product and knowledge engineering; and help shape the AI roadmap. AI or ML engineering, including shipping LLM systems that real users depend on. Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework. A repeatable approach to LLM evaluation, including representative datasets, regression testing, LLM-as-judge techniques, or human review loops. 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. Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk. Comfort with Neo4j and Cypher, or a comparable graph store, and the ability to ramp quickly on graph data modeling. Solid Python. Production habits around FastAPI, asynchronous services, testing, observability, and maintainable interfaces are required. Enterprise AI security. Prompt-injection awareness, context-leak prevention, tenant isolation, role-based access, and policy-layer separation. Experience running production AI services in Azure and using Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or a similar platform. Work across evidence, causal pathways, controls, organizational history, and corrective actions in a domain where correctness matters. Make quality measurable, observable, and improvable instead of relying on demos or intuition. Visible customer impact. Work closely with the CTO, product, and knowledge engineering, make consequential technical decisions, and see your work reach customers quickly. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. If you would like more information about how your data is processed, please contact us.

📌 Software Engineer, AI Systems (Canada)
🏢 AI Fund
📍 Canada