Role Description
San Francisco, CA · On-site · Full-time
Compensation: $180,000–$250,000 + competitive equity
About the Company ---------------------
Our client is a seed-stage AI company building systems that take on a business's hardest, multi-day, hundred-step operational processes end-to-end — and verify the results, so teams can step out of the loop. They start in finance and accounting operations at operationally complex enterprises, where "almost right" carries the same cost as being wrong: reconciliation across physical and digital systems, invoice and PO matching, and month-end close. The team is small, highly technical, and unusually close to the work — everyone talks to customers and ships full systems end-to-end.
Founded 2025 · 1–10 people · Industry: Applied AI / agentic automation for finance operations
The Role ------------
This is a post-sales Forward Deployed Engineer role focused on making the platform land with customers — implementing and building on top of it rather than doing heavy custom or core-product engineering. You'll move between shadowing a finance team to learn how they work, wiring up computer-use agents against their real systems, and showing leadership that a week of manual work now takes an hour. It's deeply customer-facing, and there's a real path to grow into leading the whole post-sales function over time.
What you'll be doing
- Embed with customer teams to learn how their operations actually run — shadowing experts, mapping decision logic, capturing tribal knowledge
- Build and deploy agents tailored to each customer's systems, edge cases, and operational reality
- Own the field-to-product feedback loop, so what you learn on the ground shapes the platform
- Present to customers and prospects with sharp narration and real design taste, sequencing demos around their specific workflows
- Deliver implementation across the post-sales lifecycle, including paid proofs of concept
Tech stack: Computer-use agents built against customer systems; a generalist range is useful — backend (Node, Postgres), frontend (React/TypeScript), and ML-adjacent work (fine-tuning / agent configuration)
Requirements ----------------
- Outstanding commercial and customer-facing instincts — this is the single most important trait for the role
- A clear, concise communicator who can translate between technical and non-technical stakeholders and read between the lines
- A genuine technical baseline — enough to understand how things work under the hood and implement well (you don't need to be a world-class engineer)
- Comfortable with ambiguity and quick to find creative solutions
- Thinks in terms of business outcomes
- 0–4 years of experience
- Able to work in person in San Francisco, mostly five days a week
Nice to Haves -----------------
- Background in consulting, solutions engineering, growth engineering, or as a technical founder
- Generalist engineering range across backend, frontend, and ML-adjacent work
- Client-facing technical operations experience (e.g. a CS background followed by a hands-on, customer-facing ops role)
- Real design and presentation taste — demo pacing, visual clarity, and narrative arc
- Experience at a strong, recognizable company, especially in a deployment or forward-deployed capacity
Why Join ------------
- Ground-floor forward-deployed role at a fast-moving seed-stage AI company, with a real path into post-sales leadership
- Work that sits right at the customer and feeds directly back into the product
- Competitive pay and equity (they aim to beat your other offers), relocation support to SF, all meals covered, top-spec hardware, unlimited PTO, full health coverage, 401(k), and an outcome-based schedule
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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