Hiring: Forward Deployed Engineer Company: Taskrabbit Location: San Francisco Bay Area Job Posted Time: 2026-09-09 18:08:40 Employment Type: Hybrid Target Skills & Keywords : Assembly, LLM, Make About the job Required Skills: •Every business function at Taskrabbit — Marketing, Customer Support, Finance, Operations — is a "customer" with real workflows, real data, and real friction. Your job is to embed with them, scope their use cases, and build the Claude-powered agent, automation, or tool that solves them. •This is an internal-facing role — there is no external customer or product work. Reporting to the Director of AI Strategy and Enablement, you'll operate the way an FDE operates at a high-growth AI company: full ownership of a deployment from discovery through production and direct accountability for whether what you ship actually changes a metric. In most cases you'll own a build end-to-end solo; in some functions you may partner with that team's own subject-matter expert to pair domain depth with technical execution. •We are hiring for engineering depth first. •This is not a role for •someone whose experience is limited to no-code/low-code automation platforms (Zapier, Make, n8n) without underlying software engineering fundamentals. The bar is: you have personally written, tested, and shipped production code that calls an LLM — ideally Claude — as part of an agent, pipeline, or internal tool, and you can speak fluently to the engineering decisions (not just the workflow decisions) behind it. •What You'll Do •Discover & scope: •Embed with a business function, run structured technical discovery, and turn an ambiguous use case idea  into a scoped build. •Build in production: •Independently design, write, test, and deploy Claude-powered agents, automations, and internal tools (Claude Code, Claude Cowork, Claude API/Agent SDK, or agentic frameworks built on Claude) — owning the full lifecycle from first commit to something running unattended in production. •Engineer for reliability, not demos: •Make and defend real engineering decisions — model selection, context/caching strategy, evals, human-in-the-loop checkpoints, error handling, cost forecasting — the difference between a working prototype and something that survives contact with real data and real users. •Establish a baseline before every build; define the metric that proves impact (time saved, error rate, throughput) and instrument for it from day one. •Leave each function more capable than you found it — train non-techn Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!