Groq builds an inference chip called the LPU, and the single most useful thing to understand before you interview there is why that chip has no cache hierarchy, no branch predictor, and no runtime scheduler. Everything it does is planned when the model is compiled. A candidate who gets that idea, and can reason about what it buys and what it costs, is most of the way to a strong loop. A candidate who shows up with a GPU mental model and a stack of memorized patterns tends to stall on the second technical round.
So this is less about grinding problems and more about whether you can think clearly about where time goes in a running model. That framing runs through nearly every question they ask.
Why the chip decides what they ask
The LPU started life as the Tensor Streaming Processor, or TSP. Jonathan Ross, who worked on Google’s first TPU, founded the company on a contrarian bet: instead of a chip that reacts to work at runtime the way a GPU does, build one where the compiler lays out every operation in advance and the hardware just executes a fixed schedule, cycle by cycle. No speculative execution, no prefetch logic, no dynamic arbitration between requests fighting for the same memory.
The payoff is predictable latency. On GPUs, batching variable-length prompts introduces jitter, because the hardware is constantly deciding what runs next and stalls waiting on memory. A statically scheduled chip removes that uncertainty. Groq has shown models like Mixtral 8x7B generating around 500 tokens per second on a single card at roughly 2 ms per token, and Llama-class models running well past 400 tokens per second on GroqCloud, several times what the same model does on an H100. Those numbers are the product pitch, and they are also the thing your interviewers live and breathe. Expect to be asked to explain them, not just recite them.
The tradeoff is real and they will probe whether you see it. Compile-time scheduling means the compiler has to know everything up front, so anything data-dependent, dynamic shapes, control flow that branches on values, sparse computation, gets awkward. On-chip SRAM is fast but small, and there is no DRAM cache to fall back on, so a model that does not fit forces you to split it across chips and pay for the interconnect. If you can name these costs without prompting, you read as someone who has actually thought about the architecture rather than skimmed the marketing page.
The roles, and which questions attach to each
Groq hires across a few tracks, and the loop gets tuned to the one you applied for. The most Groq-specific track is compiler engineering, because the compiler is where all the hard scheduling problems live. Silicon and hardware roles go deep on dataflow, pipelining, and on-chip memory. Kernel and runtime engineers write the low-level code that maps model operations onto the chip. Then there is the inference cloud side, GroqCloud, where the questions look more like classic distributed-serving and low-latency systems work: batching, autoscaling, tail latency, keeping thousands of concurrent requests fast.
A machine learning performance engineer sits in the middle of all of this, taking a model that runs and making it run faster, which means understanding quantization, operator fusion, and how a given transformer maps onto the hardware. Whichever track you are in, the interviewers assume you understand that inference and training are different problems. Inference is latency-bound and memory-bandwidth-bound; training is throughput-bound and compute-heavy. Conflating the two is a fast way to lose credibility.
The round structure
The loop is fairly standard in shape and moves at a reasonable pace. Candidates report the whole thing taking around a month from first contact to offer. What follows is the typical path for an experienced engineer; new-grad and intern loops compress it.
| Stage | Format | What it is actually testing |
|---|---|---|
| Recruiter screen | 30 minutes, phone | Motivation, background fit, whether you can talk about real projects you have shipped rather than coursework |
| Technical phone screen | 45 to 60 minutes with an engineer, often a staff engineer | Coding in a language you choose, plus a domain conversation tied to your track |
| Domain depth round | 60 minutes | Compiler, hardware, or systems depth; whiteboard reasoning about the LPU model and its tradeoffs |
| Coding and systems round | 60 minutes | Low-level or performance-sensitive code, memory layout, concurrency, complexity analysis under real constraints |
| Behavioral | 45 minutes, sometimes with a VP or hiring manager | How you handle ambiguity, disagreement, and moving fast without a spec handed to you |
One thing candidates consistently mention: Groq de-emphasizes the LeetCode grind and pays attention to your GitHub and the work you can actually point to. That does not mean there is no coding. It means the coding tends to be grounded in something the chip or the serving stack really does, and they would rather see you write clean, correct C++ that respects a memory constraint than watch you speed-run a graph puzzle you memorized.
Questions phrased the way they actually come up
The strongest signal in a Groq loop comes from open reasoning questions where there is no single right answer, only good and bad thinking. A few that map to real rounds:
- Walk me through everything that happens between a token going into the model and the next token coming out. Where does the time go, and which part would you attack first?
- Our compiler schedules every operation ahead of time. What kinds of models or operators make that hard, and what would you do about a branch that depends on a computed value?
- Here is a large matrix multiply. The chip has a fixed amount of on-chip SRAM and no DRAM cache behind it. How do you tile the work so it fits and stays busy?
- Inference latency on a GPU is noisy from request to request. Why, and how does a statically scheduled chip change that?
- You have eight chips and a model that does not fit on one. How do you split it, and where does the latency show up once you do?
For kernel and systems roles, expect something more hands-on: implement a lock-free ring buffer, reason about false sharing across cores, or explain what a cache line does to a tight loop and how you would lay data out to avoid a stall. For the GroqCloud side, the questions drift toward serving design. How do you batch requests when latency matters more than throughput? What happens to p99 when one slow request lands in a batch with fast ones? How would you autoscale a fleet where each unit of hardware is expensive and cold starts hurt?
The KV cache comes up often, because it is where inference memory actually goes. Be ready to estimate how much memory a given context length and model size consume, and to talk about why long contexts pressure the whole system. If you can do that arithmetic out loud without a calculator, it lands well.
What they screen for beyond the answer
Correctness matters, but Groq is a hardware-first company solving a young problem, and the people who do well there reason from first principles when the documented answer runs out. Interviewers watch for whether you narrate a tradeoff instead of pretending one does not exist, whether you ask what the constraints are before you start solving, and whether you can hold a systems-level view and a bit-level view at the same time. Someone who can say “this is bandwidth-bound, not compute-bound, so the fix is on the memory side” is speaking their language.
They also care that you can disagree well. Small teams building novel hardware argue constantly, and a candidate who caves the moment an interviewer pushes back, or who digs in without new evidence, both read as risks. Show your reasoning, update when you are wrong, and hold your position when you are right and can say why.
How to prep without wasting weeks
Spend your time understanding the LPU rather than drilling problem sets. Read Groq’s own architecture material and be able to explain, in your own words, what deterministic execution means and why removing caches and speculation makes latency predictable. Know the rough performance numbers and, more important, the reason behind them. If you have a project that touches performance, low-latency systems, GPU or accelerator work, a compiler, or anything close to the metal, get it clean on GitHub and be ready to walk through a real decision you made in it.
Refresh the fundamentals that a statically scheduled machine leans on: memory hierarchies, what actually costs cycles, how a matrix multiply maps onto hardware, and the mechanics of transformer inference including attention and the KV cache. For coding, keep your C++ sharp and practice writing code that respects a fixed memory budget, since that constraint shows up more than any trie or interval-merge trick.
On compensation, Groq pays competitively for a venture-backed startup, with a meaningful part of the offer in equity, which means the headline total depends heavily on how you value that equity and what stage the company is at when you join. Those figures move, so pull current ranges from levels.fyi or ask your recruiter directly rather than trusting any fixed number you read secondhand. The equity question is worth thinking through before you reach the offer stage, because it is the part of a startup package that varies the most and the part candidates most often misjudge.
If you leave the loop able to explain why a chip with no cache can beat one with a very good cache at this specific job, you understood the assignment. That single idea is the thread the entire interview is pulling on.
