FDEInterviews logoFDE/Interviews
GFORWARD DEPLOYED ENGINEER PROGRAM

Giga ML Forward Deployed Engineer interview questions

Giga ML is an applied AI lab building end-to-end customer care agents, founded by Varun Vummadi and Esha Manideep and part of Y Combinator's S23 batch. Its agents resolve support tickets at high volume for customers including Postman and Zepto. Forward Deployed Engineers are the technical interface to those customers: they lead engagements end to end and ship production agents on real data and cloud infrastructure. The role is in San Francisco and fully in-office.

16 questions to practise16 concepts to master4 core topicsrole: Forward Deployed Engineer

The Giga ML Forward Deployed Engineer interview process

Limited public data
RoleForward Deployed Engineer I/II; Senior/Staff Forward Deployed Engineer
No interview-loop breakdown found from any source. Only role postings (multiple levels: I/II and Senior/Staff) confirmed.
WHAT THEY'RE EVALUATING
  • End-to-end deployment of AI solutions for enterprise brands
  • Direct customer-facing delivery
  • Fast-moving small-team ownership

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.

Giga ML 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.

NO TRACEABLE BAND

We have not found a compensation figure for this role at Giga ML that we can trace to an employer posting or a public aggregator. Rather than publish an estimate, we are naming the gap. Their careers page is the authority, and postings in some jurisdictions are required to state a range.

HIRING FROM INDIA
Global AI lab, India-based hire

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.

LEVELREPORTED FOR THIS EMPLOYER TYPE
Junior (0-2 yrs)₹35 LPA - ₹55 LPA
Mid (3-6 yrs)₹55 LPA - ₹90 LPA
Senior (7+ yrs)₹90 LPA - ₹1.5 Cr

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.

Representative Forward Deployed Engineer questions for Giga ML's loop

Giga ML's loop draws from these tracks. Here are the highest-signal questions in each, ordered by what candidates rate most useful.

16 questions · 14 unlocked for you
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →
Open full answer →

Go deeper on the topics Giga ML's loop tests

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

RAG & Agents54 →LLM & GenAI56 →Coding & DSA93 →System Design70 →

The concepts Giga ML's Forward Deployed Engineer loop assumes you know

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

RETRIEVAL & AGENTS

Foundational
Retrieval-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.
Foundational
Vector 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 in
Hybrid 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🔒 Premium
Agent 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

Foundational
Tokenization & 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.
Foundational
The 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.
Foundational
Embeddings & 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🔒 Premium
LoRA 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.

CODING & ENGINEERING CRAFT

Foundational
Parsing Messy, Real-World DataCustomer files are dirty: inconsistent quoting, missing headers, junk rows, encodings that lie. The job is to parse defensively, skip and log bad rows instead of aborting the whole batch, and keep parsing pure and separate from business logic so it stays testable and deterministic. This is most of what early FDE data-ingestion work actually is.
Foundational
Big-O That Actually MattersOn a deployment, Big-O is not a whiteboard puzzle; it is the one calculation that tells you whether the customer's data fits in the approach you picked. The skill is spotting the term that dominates at their scale, knowing when brute force dies and you need an index or ANN, and recognizing when constant factors and memory decide the outcome instead of the exponent.
CoreSign in
Testability and Dependency InjectionCode that reaches out to the clock, the network, the filesystem, or a random generator cannot be tested deterministically, because its output depends on the world. The fix is to separate pure logic from side effects and inject the things that touch the world (the clock, I/O, randomness) so a test can pass fakes. When you inherit untestable code, pin its current behavior with a characterization test first, then refactor under that net.
CoreSign in
Streaming and BackpressureStreaming processes data one chunk at a time so memory stays flat no matter how big the input is. The moment a producer outruns its consumer, you need backpressure: a bounded buffer that makes the producer wait instead of piling unbounded work into memory. In Python this is generators and chunked reads for the streaming half, and a bounded queue (or a blocking put) for the backpressure half. Get it wrong and a 50 GB file or a fast upstream OOMs the box.

SYSTEM DESIGN FOR AI IN PRODUCTION

Foundational
From Proof-of-Concept to ProductionA notebook that answered one question correctly during a demo is not a deliverable. Production is the unglamorous work that turns a one-time success into a system the customer can run, trust, and operate without you in the room. Closing that gap is most of the FDE job.
CoreSign in
AI Cost and Unit EconomicsUnit economics is the napkin math that decides whether an AI deployment ships: cost per request driven by input and output tokens, multiplied by volume, against the human or manual baseline it replaces. It also governs the API-versus-self-host break-even, which only flips in favor of your own GPUs above a real utilization threshold.
Foundational
Retries, Exponential Backoff and JitterWhen a call fails on a transient error, retrying immediately is the worst thing you can do: it piles load onto an already-struggling service and synchronizes every client into a stampede. Exponential backoff spaces retries out, and jitter de-synchronizes the clients so they stop arriving in lockstep.
Advanced🔒 Premium
VPC and Air-Gapped DeploymentLarge enterprises will not let their data leave their security boundary, so you deploy your software inside the customer's private VPC, reach their data over PrivateLink instead of the public internet, authenticate through their SSO, and encrypt everything at rest and in transit. For truly air-gapped environments you ship self-hosted models too. This is core Forward Deployed Engineer work.
Explore the full curriculum →

Where to apply, and official Giga ML resources

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

ApplyCareers at Giga ML

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

ABOUT THE ROLE
What is a forward deployed engineer?What FDE roles payWho else is hiringThe skills FDE loops test
GIGA ML INTERVIEW FAQ
Does Giga ML hire Forward Deployed Engineers?▲

Yes. Giga ML posts Forward Deployed Engineer roles at several levels, from Forward Deployed Engineer I and II through Senior and Staff. The listings are San Francisco and in-office, and describe leading customer engagements from start to finish.

What does a Giga ML Forward Deployed Engineer do?▼
What is the Giga ML Forward Deployed Engineer salary?▼

Walk into your Giga ML Forward Deployed Engineer interview ready

Unlock every FDE interview answer, ordered easy to hard, plus the full concept curriculum, for 6 months. One payment, no auto-renewal. Free questions and concepts in each track, no card needed to start.

Unlock all answers · ₹2,000 / $25Browse free questions first

Or create a free account to unlock more free answers per topic.

Other Forward Deployed Engineer interviews to prep

Companies whose loops test the same tracks as Giga ML's.

CursorForward Deployed Engineer interview →AsanaForward Deployed Engineer interview →BCG XForward Deployed Engineer interview →CleraForward Deployed Engineer interview →CognitionForward Deployed Engineer interview →CognizantForward Deployed Engineer interview →

Independent and not affiliated with Giga ML. All trademarks belong to their owners.