Founding Forward Deployed Engineer

𝗙𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 (𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀)

Our client | San Francisco, hybrid | Full-time | $180,000 to $230,000 base + equity

𝗖𝗼𝗺𝗽𝗮𝗻𝘆: Our client, a customer command center built on AI agents

𝗥𝗼𝗹𝗲: Founding Forward Deployed Engineer. First hire in the deployment function.

𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: San Francisco, hybrid. California-based candidates strongly preferred.

𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻: $180,000 to $230,000 base, plus equity. Final offer depends on experience.

𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: 3 to 7 years in a customer-facing technical role

𝗪𝗼𝗿𝗸 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: US work authorization required. No visa sponsorship for this role.

𝗛𝗶𝗿𝗶𝗻𝗴: 1 hire now, with room to build a team. Hiring urgently.

𝗣𝗿𝗼𝗰𝗲𝘀𝘀: CTO conversation, technical case study, CEO conversation, references, offer

𝗖𝗵𝗲𝗰𝗸 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝗽𝗽𝗹𝘆

Apply if all of these are true:

• You have 3 to 7 years in a customer-facing technical role (forward deployed, solutions, implementation or customer-facing engineering).

• You personally owned customer deployments end to end.

• You have worked at a venture-backed startup in an early-stage, zero-to-one setting.

• You have built and deployed LLM agents with customers, including prompts and evals.

• You write Python with FastAPI, and React.

• You are in the San Francisco Bay Area or California, or will relocate for a hybrid role.

• You are authorized to work in the US without visa sponsorship, now or in the future.

If one of these does not describe you, this role is probably not the right fit.

𝗔𝗯𝗼𝘂𝘁 𝗼𝘂𝗿 𝗰𝗹𝗶𝗲𝗻𝘁

Our client is a customer command center built for the AI era. Older customer success tools such as Gainsight and Planhat give teams a dashboard and ask them to type data into it. Its AI agents do the work themselves: chasing renewals, running onboarding, and building quarterly business reviews.

Thousands of agents are live in production for enterprise customers and fast-growing AI companies. The company is about a year and a half old and still small. The founding team built generative AI and automation platforms at well-known technology companies, and the CTO is a third-time founder who has scaled engineering organizations past 100 engineers. The company is backed by leading venture investors, with angels from well-known AI and software companies.

𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗲𝘅𝗶𝘀𝘁𝘀

For the first stretch, one engineering team built the platform and worked directly with customers. That team has now done many of those reps, and the platform handles most of what customers need. The bottleneck has moved from building the platform to deploying it: getting each customer live, teaching them how it works, and setting their agents up against their real systems.

You will be the first person to own that function. You are technical and good with customers, and you know how agents work, how to write prompts, and how to measure whether an agent is good enough to ship.

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗱𝗼

• Own a set of customer accounts through the six-week deployment cycle, from kickoff to go-live.

• Embed with customers to understand how they run onboarding, adoption and renewals, then identify where the agents add the most value.

• Connect the platform to each customer's systems, including BigQuery, Salesforce and data warehouses. Set up SSO and integrations, and build the data pipelines.

• Build and deploy customer agents: gather requirements, write the prompts, and configure the agents against real customer data.

• Build evals that prove agent quality before you ship, then iterate until the agents clear the bar.

• Run customer meetings for roughly half your day or more, working alongside a product and deployment strategy counterpart who owns the relationship while you own the technical implementation.

• Partner with go-to-market to close, activate and expand accounts. You are the technical credibility in the room.

• Debug live deployments and run root cause analysis when an agent misbehaves in production.

• Work with the platform engineers when a customer needs a capability we do not have. For small fixes, write the ticket and the pull request yourself.

• Feed what you learn in the field back into the product. When the platform gets in the way, fix the platform.

𝗪𝗵𝗮𝘁 𝗮 𝘁𝘆𝗽𝗶𝗰𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲

Each account moves through a six-week motion split into a few phases. Depending on where an account is, you might be setting up integrations, getting data pipelines running, working through requirements, or building and tuning agents. Most deployments run virtually. Customers are mostly in California, with a growing group on the East Coast, and in-person visits happen occasionally but are not the main mode.

A normal day is about half customer meetings and half focused work. Focused time goes to blockers: a customer wants a capability the platform lacks, so you work out a workaround with the platform engineers, or build the fix yourself.

𝗪𝗵𝗮𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲

In your first 90 days:

• Every account you own goes live within the six-week timeline.

• Customers get real value from the platform and there are no fires in your accounts.

• You communicate clearly with customers about status and keep them confident in the deployment.

• When something blocks a deployment, you work out quickly whether the fix is on our client's side or the customer's, and find a creative way to keep momentum.

Between three and six months in, you take ownership of the deployment function itself. A playbook exists, and the expectation is that you improve it: spot inefficiencies, restructure how the work is done, and bring your own ideas. As the company grows, this role can grow into leading the deployment team, including hiring other forward deployed engineers.

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗮𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿

• 3 to 7 years in a customer-facing technical role: forward deployed engineer, solutions engineer, deployed AI, implementation, or customer-facing software engineer.

• Experience owning customer rollouts end to end, not only building the platform.

• Experience at a top-tier, venture-backed, high-growth startup in an early-stage, zero-to-one setting.

• Hands-on experience building and deploying LLM agents with customers: writing prompts, building evals, and running complex long-running agents.

• Strong Python with FastAPI on the backend, and React on the frontend.

• Strong communication and high EQ. You can explain complex ideas clearly and earn trust with customers.

• Strong ownership and comfort with ambiguity. You decide what to do next without a task list.

• Based in the San Francisco Bay Area or willing to relocate for a hybrid role.

• US work authorization that does not require visa sponsorship.

𝗦𝘁𝗿𝗼𝗻𝗴 𝘀𝗶𝗴𝗻𝗮𝗹𝘀

• A few years at a big tech or similarly strong engineering company, followed by two or more years at an AI startup in a customer-facing role.

• Founder or founding engineer at a YC-backed or similar company.

• A clear record of promotion and growing scope.

• Experience at companies known for forward deployed teams, such as Palantir, Databricks, Scale AI, Glean, Cohere and Snowflake.

• Earlier-career standouts with two to three years may apply only if they combine a top CS program, strong internships, and a founding engineer or forward deployed role at a YC-backed or Palantir-style company.

A specific degree is not required. We care more about the depth of your experience than the degree.

𝗧𝗵𝗶𝘀 𝗺𝗮𝘆 𝗻𝗼𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗶𝘁 𝗶𝗳

• Your experience is mostly at large, slower-moving technology companies or traditional consulting firms, with no startup or customer-facing deployment work.

• Your background is platform or infrastructure engineering with no direct customer work.

• You prefer to stay behind the keyboard and avoid customer conversations.

𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸

Python, FastAPI, React, PostgreSQL, LLMs and RAG, BigQuery, CI/CD and agent frameworks. There is no hard requirement on specific tools. Experience with agents, evals and prompt writing matters more than any single language or framework, and zero-to-one building experience is valuable.

𝗛𝗼𝘄 𝘄𝗲 𝘄𝗼𝗿𝗸

Our client is a small team that ships fast. Speed is a large part of how it has built credibility with customers and outpaced larger competitors.

The team also keeps things simple. With agents it is easy to generate a lot of code quickly, but code that has not been thought through makes the platform unstable. We work from first principles: what are we trying to do, and what is the simplest way to get there?

We value clear communication, high EQ, and people who work well both inside the team and in front of customers.

𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻

• Base salary of $180,000 to $230,000, plus equity. The range reflects experience and caliber.

• Hybrid in San Francisco. The team is currently remote and an SF hub is being set up, so expect to be in the office periodically. Customer travel is occasional.

• California-based candidates are strongly preferred. Candidates from outside California should be prepared to relocate to the Bay Area.

• Our client cannot sponsor visas for this role. Candidates must already have US work authorization.

𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗿𝗼𝗰𝗲𝘀𝘀

• Conversation with the CTO (about 30 minutes): your experience, how you communicate, and whether you can be put in front of a customer.

• Technical case study: you receive a prompt in advance and then discuss your approach with the CTO in a live session of about 60 minutes. The case reflects problems our client sees with real customers.

• Conversation with the CEO (about 30 minutes): values, ownership, how you handle ambiguity, and how you would shape the deployment function.

• Reference checks, followed by an offer.

The company is hiring urgently and moves fast through each step.

𝗛𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆

Apply on LinkedIn with your resume and answer the short screening questions. We review every application and reply within 48 hours.