Senior Forward Deployed Engineer (Full-Stack)

Dscout AI Training & Evals builds human data and evals that show whether AI works for real people. For ten years Dscout has captured how people behave in real context, with consent and scientific rigor. Dscout AI Training & Evals points that engine at AI: consented human behavior and judgment, turned into the training data, evals, and benchmarks AI teams trust.

Models ace benchmarks and still surprise people in the wild. A benchmark says how capable a model is. It does not say whether the model works for the person using it, across weeks, in the mess of real life. That gap is where we work.

We serve AI researchers, the product builders who ship alongside them, and the research teams who partner with both. We are a new unit inside an established company: live customers, a mature platform (React, Elixir, Python, Swift, Kotlin; SOC 2 and HITRUST certified), a small senior team, and room to build. We work AI-native. Agents write code, evals gate changes, and we aim to do in months what used to take years.

A note from our Chief AI Officer & GM. The hardest AI problems show up when a model meets real people, with their tools, their interruptions, and their goals. Our customers need a partner who can design the answer, build it, and ship it fast. Ground Truth sits where deployment meets research: what we learn in the field shapes the science, and the science shapes what we ship. Come build it with us.

Why this seat

• Work directly with AI labs and AI product teams on problems that have no playbook yet.
• Build on a platform that already works, so your time goes into the hard part, not the plumbing.
• Own technical decisions on your programs, and see your builds become product.
• Join a small senior team with direct access to the GM, our Principal Engineer, and the Head of AI Research.

Our Strategic Project Leads run customer programs from first scoping call to final data delivery. You are their technical partner and builder. Customers ask for things that have not been built before: weeks of real-world AI use, captured in context, delivered as data a lab can train or evaluate on. You work out how. Some of it our platform already does, some needs configuring, and some has to be built. Knowing which is the job.

Programs you will work on

• Long-horizon, real-world use of AI products, captured over days and weeks.
• Side-by-side model comparisons judged by people who match a specific profile.
• Human judgment and preference data, delivered in formats a training pipeline can ingest.
• Feedback loops that connect real user behavior to model and product decisions.

What you will do

Scope and shape programs

• Turn a fuzzy customer brief into a technical plan in days, alongside the Project Lead.
• Decide what to reuse, extend, or build, and prove the gap before you write code.
• Write the scope down: what we will deliver, how it works, what could go wrong.

Build and ship

• Build capture flows, task logic, data pipelines, and delivery across web, mobile, and backend.
• Ship production code with real participants on the other end.
• Use coding agents and evals as part of how you build, and teach the habit to others.

Build trust into the data

• Own redaction, validation, provenance, and clean delivery.
• Design quality checks and human-review paths, and measure whether they work.
• Work with our research team so the data you ship carries evidence that it is good.

Turn the field into product

• Work with core engineering to move repeat builds into the platform. No forks.
• Tell us what customers keep asking for, and help decide what we build next.

Latitude. These areas are the core, not the whole list. You will have real room to propose new program types and platform capabilities as the market moves.

A good first year

• Several customer programs scoped and shipped, with you named by the customer.
• Signed scope to first data gets noticeably faster.
• Two or more custom builds absorbed into the core platform.
• Project Leads stop asking “can we do this?” and start asking “how fast?”

How we will hire: a conversation with the GM; a scoping exercise on a real, anonymized brief (what would you reuse, what would you build); a technical deep dive with core engineers; a conversation with the Project Leads and the Head of AI Research.

What we care about most. Tools come and go. We care most about whether you can walk into a vague customer ask, find the shortest honest path to a working answer, and ship it. A particular stack or past FDE title matters less.

What you bring

• 7+ years shipping production full-stack software. We care more about what you have shipped than which languages you list.
• Experience working directly with technical customers, as a forward deployed engineer, solutions engineer, or early founding engineer.
• Experience building and running data pipelines: ingest, clean, redact, deliver.
• Hands-on time with AI coding agents (Claude Code, Cursor, Codex or similar). You have shipped serious work with them, not just used autocomplete.
• Experience building with LLMs and a sense of how they get evaluated. You are at ease with non-determinism and reason about behavior across a distribution, not a fixed test suite.
• You write clearly and can explain a tradeoff to a researcher and to a PM.
• Based in the Bay Area, with occasional customer travel.

Nice to have: human data, labeling, or eval pipeline experience; mobile or browser-based capture; sensitive or regulated data; you have founded something.

Compensation: $250-400K OTE + Equity. Includes a built-in bonus and equity program, flexible PTO, 15 holidays, 12 weeks of paid parental leave, 401k match, and an education stipend.

This is an ideal list. If the role excites you and you do not check every box, we would still love to hear from you.