Applied AI / Forward Deployed Engineer · The Role: Applied AI & Forward Deployed Engineer · Lesson 4 of 5
The interview loop — what to expect
The loop varies by company but almost always has these stages. Prepare for each:
- Recruiter / hiring-manager screen — your story, why AI, why this company. Have a crisp "here's an AI thing I shipped" narrative.
- Practical coding — usually not abstract leetcode. Expect: call an LLM API, parse/transform data, build a small RAG or tool-calling loop, or debug a broken prompt/pipeline. Python, often in a shared editor, sometimes with internet access.
- LLM / applied AI deep-dive — RAG vs fine-tuning, how to evaluate, how to stop hallucinations, prompt design, agents. Concept + judgment.
- AI system design — "design an AI assistant for X." Ingestion, retrieval, orchestration, evals, cost/latency, guardrails, feedback loops.
- Take-home or live build (common at startups) — build a small working AI app; judged on it working, code quality, and evals.
- Behavioral / customer-facing — ambiguity, dealing with an unhappy stakeholder, prioritization, communicating to non-engineers. FDE roles weight this heavily.
Company flavors (2026)Palantir (invented the role): signature Decomposition round ("design a tech to help low-vision elderly cook"), a Re-engineering round (find real bugs — and red herrings — in 500+ lines), a Learning round (build with an unfamiliar library on the spot); AI tools banned. OpenAI: ~5-hr take-home on their APIs + a recorded video walkthrough, a defense screen (rate limits, retries, "how do you know it works?"), then an open-ended customer solution design. Anthropic ("Applied AI"): practical coding (one problem, escalating levels), a take-home from a fictional customer brief (no AI unless explicitly permitted), a discovery-call simulation, and a design round centered on eval harnesses — posting language names MCP servers, sub-agents, and agent skills as deliverables. Sierra: 'AI-native interview' — a 2-hr build using any AI tools (graded on how you drive them), plus a no-AI debugging round on an unfamiliar agent codebase and a presentation round. Glean/Harvey still run LeetCode-style screens; Decagon/ElevenLabs/LangChain run practical builds + customer case studies. The Round-by-Round Playbook module drills each of these.
The 2026 twist — AI in the roomLoops split into AI-assisted rounds (Sierra, Meta, Canva, Ramp — you're graded on how well you drive and verify AI output, and your chat transcript may be reviewed) and hardened anti-AI rounds (proctored screens, live debugging, Palantir bans AI outright). Never assume: ask what's allowed before every round, and prep both modes.
◆ Lock it in
- Coding rounds are practical (API calls, RAG, data wrangling) — not abstract leetcode.
- Expect an applied-LLM deep dive and an AI system design round.
- FDE loops weight behavioral / customer-facing heavily — prepare STAR stories.
Feynman drill — say it out loudRecite the six standard stages of an Applied AI / FDE loop from memory, with one sentence on how you'd prep each.
Step 1 rate your confidence · Step 2 pick your answer
You're prepping for the coding round of an Applied AI Engineer loop. Best use of time?
Step 1 — how sure are you?