The complete interview prep guide for Forward Deployed Engineer roles at Palantir, Databricks, Scale AI, Anduril, Google, and beyond.
A Forward Deployed Engineer (FDE) is a hybrid role: part software engineer, part solutions architect, part customer-facing consultant. FDEs embed directly with enterprise clients to deploy, customize, and extend complex software and AI systems.
Unlike traditional SWEs, FDEs are expected to:
- Write production code on-site at customer locations
- Run live technical demos and architecture reviews
- Translate ambiguous business problems into working systems
- Manage relationships with technical and non-technical stakeholders
FDE interviews test a unique combination of skills: agentic system design, live demo readiness, customer-facing communication, and LLM deployment — not just algorithms.
- Behavioral
- System Design
- Coding
- GenAI Architecture
- Customer-Facing Case Study
- Company Guides
- Contributing
FDE behavioral interviews use STAR format but focus on customer impact, ambiguity, and cross-functional influence — not just internal team work.
| # | Question |
|---|---|
| 1 | Tell me about a time you had to push back on a client request. |
| 2 | Tell me about a time you explained a technical issue to a non-technical audience. |
| 3 | Tell me about a time you earned customer trust after something went wrong. |
| 4 | Tell me about a time you had to balance customer customization with product scalability. |
| 5 | Tell me about a time you handled ambiguity. |
| 6 | Tell me about a time you worked with a difficult stakeholder. |
| 7 | Tell me about a time you had to prioritize between multiple urgent customer requests. |
| 8 | Tell me about a time you had to make a trade-off between speed, quality, and scope. |
| 9 | Tell me about a time you built something with incomplete information. |
| 10 | Tell me about a time you led without formal authority. |
| 11 | Tell me about a time you improved a process, not just a piece of code. |
| 12 | Tell me about a time you handled missing, messy, or inconsistent data. |
| 13 | Give an example of when you influenced without authority. |
| 14 | How would you plan a 30-day pilot for an enterprise GenAI assistant? |
| 15 | How would you present architecture trade-offs between accuracy, latency, cost, and safety to a non-technical exec? |
| 16 | How would you communicate that the system is not ready for production? |
| 17 | How would you align security, legal, product, and engineering stakeholders? |
| 18 | How would you handle a customer who wants full automation but has no evaluation data? |
| 19 | How would you manage scope when a customer asks the assistant to do everything? |
| 20 | How would you structure a customer workshop for GenAI workflow discovery? |
| 21 | How would you create an implementation roadmap from prototype to production? |
| 22 | How would you convert interview ambiguity into a clear answer without asking endless questions? |
→ See all 34 behavioral questions + frameworks at fdehandbook.com
FDE system design questions emphasize observability, security, multi-tenancy, and cost control — the real constraints of enterprise deployments.
| # | Question |
|---|---|
| 1 | Design a real-time supply chain visibility platform for a Fortune 500. |
| 2 | Design an audit logging system that is tamper-proof. |
| 3 | How would you design permission-aware retrieval for enterprise documents? |
| 4 | How would you defend a RAG system against prompt injection hidden inside retrieved documents? |
| 5 | How would you prevent cross-tenant data leakage in a multi-tenant GenAI platform? |
| 6 | A customer asks whether the LLM provider can train on their data. How do you respond architecturally? |
| 7 | How would you handle PII in prompts, logs, and evaluation datasets? |
| 8 | How would you design least-privilege access for model, retrieval, and tool layers? |
| 9 | How would you prepare a GenAI system for a security review by a regulated enterprise customer? |
| 10 | Design tool permissions for an agent that can read tickets, draft responses, and update records. |
| 11 | A GenAI assistant has p95 latency of 18 seconds. How would you reduce it? |
| 12 | A customer is worried about unpredictable LLM cost. How would you control it? |
| 13 | How would you design model routing between small and large models? |
| 14 | How would you use caching safely in a permission-sensitive enterprise assistant? |
| 15 | An agentic workflow costs too much because of repeated tool and model calls. What do you do? |
| 16 | How would you design graceful degradation when the LLM provider is slow or unavailable? |
| 17 | How would you scale a RAG system for thousands of concurrent users? |
| 18 | How would you instrument traces across retrieval, prompt construction, model calls, and tools? |
| 19 | How would you debug a customer report that the assistant gave a hallucinated answer? |
| 20 | How would you monitor cost, latency, and quality together? |
| 21 | How would you design alerting for a GenAI system without creating noise? |
| 22 | How would you build a production support playbook for a GenAI pilot? |
→ See all 30 system design questions + sample answers at fdehandbook.com
FDE coding questions are practical and deployment-focused — less LeetCode, more real engineering challenges you'll face on-site.
| # | Question |
|---|---|
| 1 | Write a SQL query to find the top 3 SKUs by revenue per region. |
| 2 | Given a CSV of IoT sensor readings, detect anomalies in Python. |
| 3 | Deduplicate webhook events — same event delivered multiple times. |
| 4 | Normalize messy CRM records with inconsistent name/email formats. |
| 5 | Validate JSON tool-call arguments before executing a write operation. |
| 6 | Sanitize PII from documents before sending to an LLM. |
| 7 | Detect prompt injection patterns in user input. |
| 8 | Implement conversation memory trimming when context exceeds the token limit. |
| 9 | Merge streaming partial responses from multiple concurrent LLM calls. |
| 10 | Calculate token cost per customer across a multi-tenant deployment. |
| 11 | Chunk documents with configurable overlap for a RAG ingestion pipeline. |
| 12 | Filter RAG chunks by user permissions before returning results. |
| 13 | Rate-limit model calls per tenant with a sliding window. |
| 14 | Implement retry with exponential backoff for failed LLM API calls. |
| 15 | Build a simple LRU cache for identical prompt + context pairs. |
| 16 | Score hallucination risk from a model response against a retrieved context. |
| 17 | Implement agent tool execution safety — validate arguments before any write. |
| 18 | Implement a multi-step workflow state machine for an agentic pipeline. |
| 19 | Implement model fallback logic — switch providers on timeout or error. |
| 20 | Build a human-in-the-loop approval queue for high-risk agent actions. |
| 21 | Reconcile CRM and data warehouse records with conflicting update timestamps. |
| 22 | Implement a rate limiter with a sliding window in TypeScript. |
→ See all 50 coding questions + solutions at fdehandbook.com
The most FDE-specific category. These questions test your ability to design, debug, and evaluate LLM systems in production enterprise environments.
| # | Question |
|---|---|
| 1 | Design a RAG pipeline for a customer support chatbot with 10M documents. |
| 2 | How would you evaluate and prevent hallucination in a production LLM app? |
| 3 | A customer needs on-premise deployment for sensitive documents. How does your architecture change? |
| 4 | Design the backend for a customer-facing AI support copilot serving multiple enterprise tenants. |
| 5 | How would you design a model gateway supporting multiple LLM providers, prompt versions, and safety controls? |
| 6 | How would you choose chunking, metadata, and retrieval strategy for long policy documents? |
| 7 | A RAG assistant gives plausible answers but often cites irrelevant sources. How would you fix it? |
| 8 | Design a multi-tenant RAG system with isolated data and per-tenant embedding configurations. |
| 9 | How would you evaluate whether hybrid search is better than vector-only search for a customer corpus? |
| 10 | A customer wants citations that legal reviewers can trust. How would you design citation handling? |
| 11 | How would you handle queries where the answer is not present in the knowledge base? |
| 12 | Design an agent that triages support tickets, searches knowledge, drafts replies, and escalates risky cases. |
| 13 | A customer wants an agent to update CRM records after meetings. How would you make it safe? |
| 14 | Design an agentic workflow for invoice exception handling with human approval. |
| 15 | How would you prevent an agent from taking unauthorized actions through tools? |
| 16 | How would you implement memory for an enterprise agent without creating privacy or correctness problems? |
| 17 | An agent gets stuck in loops and calls tools repeatedly. How would you fix the design? |
| 18 | When would you choose a deterministic workflow instead of an agent? |
| 19 | How would you design an evaluation framework for a GenAI solution before production? |
| 20 | How would you build a golden dataset for a RAG-based enterprise assistant? |
| 21 | How would you measure hallucination and groundedness in production? |
| 22 | A model upgrade improves fluency but worsens policy accuracy. How do you decide whether to ship? |
| 23 | A prompt-injection test succeeds against your RAG assistant. What is your incident response? |
| 24 | A tool-using agent updates the wrong record. How would you investigate and prevent recurrence? |
| 25 | A customer pilot has low adoption despite good technical metrics. How do you diagnose it? |
→ See all 50 GenAI architecture questions + deep-dive answers at fdehandbook.com
FDE case studies simulate the real job: decompose a vague business problem, propose a system, and defend it to a mixed technical/non-technical audience.
| # | Question |
|---|---|
| 1 | A logistics company wants to reduce detention fees by 20%. How do you approach this? |
| 2 | Your client's ops team refuses to adopt the software you deployed. What do you do? |
| 3 | A sales leader asks for a GenAI tool to summarize every customer call and auto-update Salesforce. What do you build? |
| 4 | A legal team wants a contract-review assistant but can't define what decisions it should make. What do you do? |
| 5 | An executive asks for an agent that can answer any business question across all company data. What do you do? |
| 6 | A bank wants an AI assistant to help relationship managers prep for client meetings using CRM, emails, and policy docs. |
| 7 | A healthcare ops team wants to automate prior-authorization review using internal guidelines and patient notes. |
| 8 | A manufacturing customer wants an AI system to diagnose machine downtime from tickets, sensor data, and manuals. |
| 9 | An insurance team wants claim handlers to use an AI assistant for coverage questions and next-step recommendations. |
| 10 | A support org wants to reduce ticket volume with an internal knowledge assistant for agents. |
| 11 | Design an Enterprise Sales Assistant using CRM data, call transcripts, emails, and RAG. |
| 12 | Design a Customer Support Agent for Zendesk / ServiceNow. |
| 13 | Design a Financial Document Analysis Assistant with strict compliance controls. |
| 14 | Design a Legal Contract Review Copilot with human approval gates. |
| 15 | Design an AI Incident Triage Bot for SRE / DevOps teams. |
| 16 | Design a multi-tenant SaaS GenAI assistant with per-customer data isolation. |
| 17 | A recruiter tells you the final round will focus on building an agentic workflow for a customer problem. How do you prep? |
| 18 | Prioritize a backlog of 12 feature requests from 3 different enterprise clients. |
→ See all 31 case study questions + structured frameworks at fdehandbook.com
Deep-dive interview guides for the companies most known for FDE hiring:
| Company | Guide |
|---|---|
| Palantir | Palantir FDE Interview Guide |
| Databricks | Databricks FDE Interview Guide |
| Scale AI | Scale AI FDE Interview Guide |
| Anduril | Anduril FDE Interview Guide |
| Google FDE Interview Guide |
Recently interviewed for an FDE role? Submit a debrief — anonymously share the questions you were asked.
- Open an Issue with the title
[Debrief] Company Name - Include: interview stage, question topics (not verbatim), difficulty, and outcome (optional)
All community contributions help keep this resource current.
This repo contains a curated sample. The complete handbook at fdehandbook.com includes:
- ✅ 195 questions across all 5 categories
- ✅ Detailed answer frameworks and sample responses
- ✅ Company-specific prep guides (Palantir, Databricks, Scale AI, Anduril, Google)
- ✅ Weekly FDE job market data
- ✅ Discord community
Built for engineers preparing for FDE, Solutions Engineer, Customer Engineer, Field Engineer, and Applied AI Engineer roles at top tech companies.