Staff Forward Deployed Engineer
$168,912–$273,368 year
HybridSan Francisco, California, United States
San Francisco, California, United StatesHybridFull Time$168,912–$273,368 yearSenior LevelHealthcare TechSmall
Full TimeSenior LevelSmallHealthcare Tech
Job Summary
Partner with customers to scope and architect AI solutions, then embed with their teams to integrate data and build production-grade pipelines. Design and ship LLM and agent-powered systems that operate reliably in the field, while simultaneously hardening these patterns into the shared platform's knowledge layer and agent frameworks. Own the flywheel by translating field learnings into reusable tooling and building evals to measure quality. Work remotely or on-site as needed, with occasional travel to customer sites.
Required Qualifications
- 5+ years building production software and data systems
- An architect's instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it
- Genuine AI/LLM depth — you've built real systems with LLMs and agents (retrieval/RAG, tool-use) and you evaluate quality rather than eyeball it
- Real data-engineering depth: building and operating data pipelines, modeling messy enterprise data, and working in a modern cloud data platform (Databricks, BigQuery, Snowflake, or similar)
- Range across both modes — you genuinely like being in front of customers and going heads-down to build reusable infrastructure, and you can switch between them without one suffering
- Customer-facing comfort: you work well with a customer's engineers and data teams — running working sessions, explaining your thinking, and earning trust through what you deliver
- A bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10-20%)
- This is a hybrid role based in the San Francisco office (2 days/week)
- This position is not eligible for company sponsorship
Desired Qualifications
- Experience in grocery, retail, or supply chain data domains
- Knowledge graphs, ontologies, or semantic layers in production; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search
- MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps, model serving, and observability for LLM systems
- Prior forward-deployed, solutions, or implementation engineering — or early-stage startup experience navigating rapid customer expansion
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