Applied AI / Forward Deployed Engineer · The Role: Applied AI & Forward Deployed Engineer · Lesson 3 of 5
A day in the life & what companies actually want
Across OpenAI, Anthropic, Palantir, Sierra, Glean, and countless startups, the day-to-day rhymes:
- Scope a fuzzy problem with a customer or PM ("can AI triage our support tickets?").
- Prototype fast — a prompt + a bit of retrieval + a script — and demo within days.
- Build an eval set from real examples so you can tell if changes help.
- Harden it: handle failure cases, add guardrails, cut latency and cost, add observability.
- Iterate with the customer, then hand off or scale it.
What every job posting is really screening for
- Speed & pragmatism — can you get to a working demo fast, not a perfect design doc.
- Ambiguity tolerance — the spec is vague and the data is dirty. You thrive anyway.
- LLM system fluency — RAG, agents, evals, cost/latency are second nature.
- Communication — you can explain tradeoffs to a CTO and a non-technical ops lead in the same meeting.
- Ownership — you carry a problem from "vague ask" to "in production" without hand-holding.
Common rejection reasonCandidates who can discuss transformers in the abstract but freeze on "the model keeps hallucinating account numbers in production — what do you actually do?" The role rewards applied judgment over theory.
◆ Lock it in
- Loop: scope → prototype → eval → harden → iterate, on customer-real data.
- Screened for: speed, ambiguity tolerance, LLM system fluency, communication, ownership.
- Applied judgment beats textbook theory in this role.
Feynman drill — say it out loudDescribe the five stages of an FDE's typical project loop from memory, in order.
Step 1 rate your confidence · Step 2 pick your answer
Which trait is MOST distinctive of a strong FDE (vs a generic backend engineer)?
Step 1 — how sure are you?