Forward Deployed Engineer

NimbleWay•San Francisco, CA

3d•Hybrid

About The Position

This is Nimble’s founding Forward Deployed Engineer role. You will build AI agents and search integrations with customers and take them into production. After qualification, you will partner with the Solution Consultant and customer engineers to refine the architecture and agree on success criteria. You will build and evaluate the first working version using the customer’s real queries, workflows, codebase, and evaluation set, then lead implementation through production deployment and an agreed handoff. A typical engagement lasts 12 weeks. You will own technical decisions for each engagement, including implementation architecture, orchestration approach, evaluations, technical scope and sequencing, and what can be reused versus what must remain customer-specific. As the first member of the FDE team, you will also help define the function’s strategy, engagement model, playbook, and future hiring profile. The role reports to the Director of Solution Consulting and does not carry a sales quota. The role is based in New York or San Francisco, with three days per week in the office. Base pay is $170,000 to $235,000 depending on level, plus 10% to 15% variable pay and equity. Nimble describes itself as a company leading the AI web search category. Its products provide businesses with accurate, auditable programmatic access to the web for building AI agents. The company says it has raised over $70 million and serves hundreds of enterprise customers.

Requirements

At least 4 years of software engineering experience.
Ability to write production Python and read and modify TypeScript.
Experience shipping and operating LLM-powered applications for real users, including explaining how failures, latency, and cost were managed.
Experience designing or improving evaluations for an LLM or other nondeterministic system and using results to make engineering decisions.
Experience building tools for agents to call through MCP, function calling, or an equivalent approach.
Shipped LLM or agent work that can be demonstrated through a repository, live product, or case study.
Experience working directly with customers or external partners to scope and deliver technical projects.
Ability to conduct discovery with a VP and debug a failing run with an engineer.
Comfort with ambiguity and rapid change in a fast-paced startup.

Nice To Haves

Experience with retrieval and RAG, including hybrid search, reranking, and freshness.
Experience with LangGraph, the OpenAI Agents SDK, the Vercel AI SDK, or the Claude Agent SDK.
Experience delivering data to Snowflake or Databricks.
Experience with web data, including scraping, anti-bot systems, and structured extraction.
Effective use of AI development tools, with responsibility for reviewing, testing, and maintaining generated code.

Responsibilities

Turn customer goals into working flows of Search, Extract, and Web Search Agent calls integrated into the customer’s code.
Build the first implementation using the customer’s real queries alongside the customer’s engineer.
Define representative evaluation sets and success criteria with customers.
Benchmark quality, latency, and cost during iteration, and instrument production workflows to detect failures and regressions.
Write production orchestration code, including fan-out, deduplication, retries, and cost caps.
Integrate Nimble into customer agents through its MCP server, SDKs, or API, on a schedule or on demand.
Lead implementation through production deployment and an agreed handoff.
Present results to customer engineers and executives and document the technical win with the Solution Consultant.
Write runbooks, train customer engineers, and hand accounts to Customer Success.
Identify recurring customer needs and turn successful implementations into reusable integrations, evaluation tools, reference applications, and product improvements.
Shape the FDE function, including its engagement model, entry and exit criteria, playbook, and future hiring profile.