Cresta Forward Deployed Engineer Interview Questions & Experience

Cresta builds AI for contact centres, combining live agent assistance with automation of the conversations and processes behind it. It was co-founded by Sebastian Thrun, with CEO Ping Wu previously co-founding Google Contact Center AI and Vertex AI, and co-founder Tim Shi an early member of OpenAI. Its Forward Deployed Engineer and Senior Forward Deployed Engineer roles sit on the AI Agent team, building and integrating agents for named customers and feeding what breaks back into the core platform.

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The Cresta Forward Deployed Engineer interview process

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Public guidance on the Cresta Forward Deployed Engineer interview experience, including round formats and preparation priorities. Last reviewed August 24, 2026.

• 1Resume review (company-published)Initial resume screen by the recruiting team.
• 2Self-paced CoderPad technical assessment (company-published)Per the posting, verbatim: the first step for qualified applicants after resume review is a self-paced CoderPad assessment to 'showcase your practical coding and problem-solving skills in a real-world environment before moving into conversational interview rounds.'
• 3Conversational interview rounds (company-published, unspecified)After the CoderPad assessment candidates move into conversational rounds; the posting does not enumerate them.

• Practical Python and Golang coding
• AI-agent / LLM / RAG / function-calling system building
• API/webhook/data-pipeline integration in enterprise environments
• Customer-facing delivery and cross-functional collaboration with PMs

The number and content of the post-assessment conversational rounds are not published, and the Blind thread where candidates asked about the process contains only unanswered questions. The application also includes two required written questions (a customer-deployment story and a cross-functional-collaboration story). Remote role; Cresta discloses it was 'born from the Stanford AI Lab' and has raised more than $270M from a16z, Greylock and Sequoia.

Compiled from our research and publicly available information (candidate reports and company interview guides). Interview loops change and are continuously iterated, and they vary by team, level, and region. Treat this as directional preparation, not an official spec, and confirm the exact rounds with your recruiter or hiring point of contact.

Cresta Forward Deployed Engineer salary

What we can trace, labelled by where it came from. We publish a band only where there is a source behind it, so some of this page is a gap rather than a number.

We do not have a verified compensation band for this role at Cresta recorded in this profile. Check the current posting for the role, level and location before comparing offers. Their careers page is the authority, and postings in some jurisdictions are required to state a range.

A US or EU AI company with no large India engineering centre. An India-based hire here is usually a global-remote contract, often USD-denominated, which is the highest-paying route into the role from India and also the hardest to get.

Reported range for this type of employer, not a figure reported for this company. Whether an India-based hire is possible at all depends on their entity and visa position, so check their careers page before you plan around it.

Full method, US bands by level, and the three India tiers side by side are in the FDE salary guide, including what actually moves your number between these tiers.

Questions modeled on Cresta loops

More from the tracks Cresta's loop tests

The highest-signal questions across Cresta's core tracks.

Go deeper on the topics Cresta's loop tests

The tracks that map to a Cresta Forward Deployed Engineer loop, ordered easy to hard.

The concepts Cresta's Forward Deployed Engineer loop assumes you know

The vocabulary and mental models behind Cresta's questions, from our curriculum. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

RETRIEVAL & AGENTS

FoundationalRetrieval-Augmented Generation (RAG)RAG grounds a language model in your own data by retrieving relevant passages at query time and putting them in the prompt, so the model answers from real sources instead of memory. It is the default pattern for almost every enterprise FDE deployment, which is why nearly every loop tests it.

FoundationalVector DatabasesA vector database stores embeddings alongside metadata and answers nearest-neighbor queries fast using approximate indexes. The real interview question is not how they work but when you actually need one instead of a library or plain Postgres with pgvector.

CoreSign inHybrid Search (Lexical + Vector)Hybrid search runs a keyword retriever (BM25) and a dense vector retriever side by side, then merges their result lists, because each one misses cases the other catches. Vectors lose exact codes and rare jargon, BM25 loses paraphrase, and combining them with Reciprocal Rank Fusion usually beats either alone.

Advanced🔒 PremiumAgent MemoryAgent memory is how an agent carries state across turns and sessions. Short-term memory is the conversation and scratchpad living inside the context window, bounded and expensive. Long-term memory is an external store the agent writes to and retrieves from on demand, usually via RAG, so it can recall facts from last week without holding them in the prompt. FDE loops probe this because the hard parts, summarization, what to persist, and stale or contradictory memory, are where agents quietly break.

FOUNDATIONS OF LLMS & GENAI

FoundationalTokenization & TokensA language model does not read characters or words. It reads tokens: sub-word chunks produced by a tokenizer, each mapped to an integer the model embeds. Tokens are the unit of the context window and of billing, and the way text splits into them explains a surprising number of model quirks, which is why almost every loop opens here.

FoundationalThe Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.

FoundationalEmbeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.

Advanced🔒 PremiumLoRA and Parameter-Efficient Fine-tuningFull fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.

MLOPS & LIFECYCLE

CoreSign inData and Concept DriftA model can lose accuracy two ways: the inputs it sees start looking different (data drift), or the true mapping from inputs to outputs changes underneath it (concept drift). The fix differs, so the FDE skill is diagnosing which one you have before reaching for a retrain.

CoreSign inModel Registry and PromotionA model registry is the source of truth for every model version, what data and code produced it, and how it scored on your eval suite. Promotion is the gated path from a registered candidate to live serving: pass the gates, soak in shadow or canary, then swap an alias so traffic moves atomically and rollback is one step.

CoreSign inCI/CD for ModelsModel CI/CD looks like code CI/CD but ships data, weights, and prompts together, and its merge gate is an eval suite against a golden set, not a passing unit test. The pipeline trains, evaluates, registers, soaks in shadow or canary, then promotes, with every input versioned so any release is reproducible.

Advanced🔒 PremiumFeature StoresA feature store is a central place that computes a feature once and serves it to both training (offline, batch) and serving (online, low-latency) from the same definition, which kills the most common production bug in ML: train/serve skew. It also handles point-in-time correctness so backfills do not leak the future. The honest catch is that most early-stage teams do not need one.

THE CUSTOMER-FACING CRAFT

FoundationalRequirements DiscoveryRequirements discovery is the work of finding the real problem hiding behind the customer's stated ask. The request they hand you ("build us a chatbot") is almost never the need; the FDE who surfaces who uses it, what success looks like, what data actually exists, and why the deadline is the deadline is the one who ships something people use.

FoundationalScoping Ambiguous ProblemsScoping an open-ended prompt ("a city wants to reduce 911 response times") is a structured move, not a flash of inspiration: clarify inputs and constraints, state your assumptions out loud, carve out the smallest useful MVP, name the accuracy/cost/latency trade-offs you are choosing, and plan for what happens when it fails. Diving straight into a model or an architecture is the most common reason candidates get cut in the simulation round.

FoundationalExplaining Trade-offs to Non-EngineersAn exec does not care whether you chose RAG or fine-tuning; they care what it costs, when it ships, and what it might get wrong. Translating a technical trade-off means converting accuracy, cost, and latency into the decision the business is actually making, framing each option as a choice with a consequence in their terms, and answering the question they will all eventually ask: why does the AI give a different answer every time, and why is that not a bug.

Advanced🔒 PremiumPilot Acceptance CriteriaPilot acceptance criteria connect a test result to a bounded deployment decision. Define eligible work, failure limits, missing evidence and the people who can approve the next step.

Where to apply, and official Cresta resources

Straight from Cresta: open roles and the company's own hiring guidance. Prep here, then apply there.

External links to Cresta's own pages. Roles and processes change; always confirm on the official site.

Senior Forward Deployed Engineer (AI Agent). Stages: Resume review (company-published) → Self-paced CoderPad technical assessment (company-published) → Conversational interview rounds (company-published, unspecified). Key focus: Practical Python and Golang coding. Compiled from public reports; loops change over time, so confirm the exact rounds with your recruiter.

Yes. Cresta posts Forward Deployed Engineer and Senior Forward Deployed Engineer roles on its AI Agent team, in San Francisco, remote in the US, and the UK. The role is described as developing agents, integrating them with external systems, and supporting deployments hands-on.

Working directly with customers alongside software and machine learning engineers to land agent deployments, then improving the core platform from what those deployments expose. Cresta frames it around cost, security and reliability rather than demos, which is the usual dividing line in contact-centre work.

Cresta's posting for the senior level lists a base range of roughly 185,000 to 235,000 US dollars plus annual bonus and equity. Bands differ by level and location, so read it as published rather than guaranteed.

Walk into your Cresta Forward Deployed Engineer interview ready

Prepare across the full loop with worked answers and the curriculum behind them. Start with the free questions and concepts to judge the depth.

Every answer, concept and course, every paper breakdown, all published video lessons, hands-on FDE Lab missions, Premium PDF guides and companion files, the full practice-test bank and work-sample downloads. Referral Premium excludes guide PDFs and their companion files.

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