Greptile builds an AI code reviewer that reads a pull request against the whole codebase rather than just the diff. Founded by Daksh Gupta and part of Y Combinator's W24 batch, it raised a Series A led by Benchmark and reports thousands of customers including Brex, Whoop and Substack. Its first Forward Deployed Engineer hire is aimed at enterprise adoption: technical discovery, tailoring Greptile to each customer's stack across GitHub, GitLab, Bitbucket, Perforce and on-premise setups, and running pilots.
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The Greptile Forward Deployed Engineer interview process
Limited public data
- Technical discovery and tailoring Greptile to each customer's stack (GitHub, GitLab, Bitbucket, Perforce, on-prem)
- Running POCs/pilots in complex environments
- Security/compliance diligence (questionnaires, architecture docs, procurement)
- Building reference integrations; voice-of-customer to product
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.
Greptile 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 Greptile 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 Greptile loops
More from the tracks Greptile's loop tests
The highest-signal questions across Greptile's core tracks.
Go deeper on the topics Greptile's loop tests
The tracks that map to a Greptile Forward Deployed Engineer loop, ordered easy to hard.
The concepts Greptile's Forward Deployed Engineer loop assumes you know
The vocabulary and mental models behind Greptile's questions, from our curriculum. Start with the foundations free; the deeper, interview-defining ideas are part of premium.
CODING & ENGINEERING CRAFT
FoundationalParsing 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.
FoundationalBig-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 inTestability 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 inStreaming 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.
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.
SYSTEM DESIGN FOR AI IN PRODUCTION
FoundationalFrom 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 inAI 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.
FoundationalRetries, 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🔒 PremiumVPC 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 air-gapped environments you ship self-hosted models too. This is core Forward Deployed Engineer work.
AI SECURITY, PRIVACY & GOVERNANCE
CoreSign inPrompt Injection and DefensePrompt injection is the attack where untrusted text smuggles instructions into a model's context and overrides the system's intent. It comes in two flavors: direct, where the user types the attack, and indirect, where a poisoned document or tool output the model later reads carries it. You cannot fully prevent it, so a competent FDE designs the system so that a successful injection cannot reach anything that matters.
CoreSign inPII Handling and RedactionPersonal data leaks into AI systems through three doors: the prompt you send a model API, the logs you keep for debugging, and the traces you store for evaluation. Handling it means detecting and redacting personal data before it crosses any of those boundaries, then minimizing, encrypting, access-controlling, and expiring whatever you must keep. In regulated industries, logging a raw prompt is the single most common compliance failure.
CoreSign inDifferential PrivacyDifferential privacy is a mathematical guarantee that the output of a computation barely changes whether or not any single person's record was included, so an attacker studying the output cannot confidently tell who was in the data. You buy this guarantee by adding calibrated random noise, and you pay for it in accuracy. The privacy budget epsilon sets the exchange rate; smaller epsilon means more noise and more privacy, and a value like epsilon = 8 is moderate, not strong.
Advanced🔒 PremiumMulti-Tenancy and Data IsolationMulti-tenancy is serving many customers from shared infrastructure while guaranteeing no tenant can ever see another's data. The isolation strategies run a spectrum from row-level filtering to fully separate databases, trading cost against blast radius. The non-negotiable rule for AI systems: tenant scope is enforced below the model, in code that filters queries and scopes credentials, never by instructing the model in a prompt. A single prompt-injected document is enough to break prompt-level isolation.
Where to apply, and official Greptile resources
Straight from Greptile: open roles and the company's own hiring guidance. Prep here, then apply there.
External links to Greptile's own pages. Roles and processes change; always confirm on the official site.
Yes. Greptile has posted a Software Engineer, Forward Deployed role, described as its first forward deployed hire, focused on helping enterprise customers evaluate and adopt the product. It is a small company, so the role spans discovery, pilots and integration rather than one slice.
Running technical discovery, tailoring Greptile to a customer's stack across GitHub, GitLab, Bitbucket, Perforce and on-premise deployments, leading proof-of-concepts, handling security and compliance diligence, and building reference integrations. The scope is smaller than a Palantir deployment and moves faster.
We have no confirmed public breakdown of the Greptile loop. The posting itself points at code-level fluency, integration work across source-control platforms, and being able to carry a security review. Prepare those plus a clear story about running a pilot to a decision.
Greptile has not published a band we can confirm for its first forward deployed hire. It is an early-stage company that has raised a Series A, so equity is likely to be a significant part of the offer relative to base.
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