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David Joseph & Company

Founding Forward Deployed Engineer

$180K - $230K San Francisco, CA, US Mid Posted August 20, 2026

Role Description

SF Bay Area, CA · Hybrid · Full-time

Compensation: $180K–$230K base + competitive equity

About the Company

Our client is a seed-stage AI startup rebuilding B2B customer operations around long-running AI agents. Instead of the usual support chatbots or pre-sales tooling, they focus on the messy post-sale lifecycle — onboarding, adoption, QBRs, renewals, and churn — deploying agents that deliver high-touch service at scale. They are backed by well-known venture investors and already have enterprise customers running the product in production.

Founded 2025 · small founding team · Industry: AI, B2B, Enterprise

The Role

We are hiring a Founding Forward Deployed Engineer to embed directly with enterprise customers and take our client's AI agents from scoping to production. You will own accounts end-to-end and feed everything you learn in the field back into the core product — the technical heartbeat of a fast-growing applied-AI team.

What you'll be doing

  • Embed with customers to learn how they run onboarding, adoption, and renewals, then identify where the agents drive the most value
  • Deploy and configure agents against real customer systems — connecting to data sources (warehouses, CRM, BigQuery), wiring up SSO and integrations, and getting agents into production
  • Build evals against real customer data to prove agent quality, and iterate until they clear the bar
  • Partner with the go-to-market team as the technical credibility in customer conversations, helping close, activate, and expand accounts
  • Debug live deployments, run root-cause analysis, and improve the platform when it gets in the way
  • Own the smallest end-to-end slice that proves value, then expand from there

Tech stack: Python, FastAPI, React, PostgreSQL, LLMs, RAG, BigQuery, CI/CD, agent frameworks

Requirements

  • Roughly 2–7 years across full-stack software engineering and/or forward-deployed engineering
  • Either a senior profile (about 5–7 years, with a couple of years each in software engineering and in a forward-deployed / customer-facing role at a strong company or startup) or a high-slope earlier-career profile (2–3 years with standout internships, an early promotion track, or founding-engineer experience at a venture-backed startup)
  • A track record of shipping AI/LLM features end-to-end into production — real systems, not just prototypes
  • Direct experience working inside customer environments (forward-deployed, solutions engineering, or similar)
  • Strong Python on the backend (FastAPI) and React on the frontend
  • A CS or Engineering degree, or equivalent skill proven by a history of shipping production systems
  • Based in, or willing to relocate to, the SF Bay Area for hybrid work

Nice to Haves

  • Experience building evals to measure and hill-climb LLM or agent quality
  • Familiarity with agent frameworks, RAG, and connecting AI systems to customer data sources and warehouses
  • Comfort partnering with go-to-market teams and being the technical voice in the room with customers

Why Join

  • A founding role with broad ownership over product and infrastructure, and real architectural influence
  • Fast feedback loops, strong promotion potential, and deep hands-on exposure to applied AI — voice agents, memory systems, knowledge graphs, and agentic workflows
  • Competitive base plus meaningful equity at an early-stage, well-backed company with genuine enterprise traction

Details

  • Location: SF Bay Area, CA
  • Work policy: Hybrid
  • Compensation: $180K–$230K + equity
  • Visa sponsorship: Not available (US citizens / Green Card holders only)
  • Employment type: Full-time

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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