How Mistral AI Interviews in 2026: Process, Questions & What They Score

1. 01

   Explain transformer internals and implement top-p decoding.

   Tests transformer internals and nucleus (top-p) sampling over a sorted probability distribution.

2. 02

   What is a decorator? (In Python)

   Tests Python decorators: functions that wrap other functions, closures and higher-order functions.

3. 03

   How do you use an image classification algorithm?

   Tests applied ML knowledge of using an image classification model.

4. 04

   Implement multi-head self-attention (MHSA) from scratch in PyTorch.

   Tests attention mechanics in PyTorch: projections, scaled dot-product, batching and a causal mask.

5. 05

   Fix a bug in a transformer block.

   Tests debugging a transformer block, where the reported bug involved pre-norm wiring with residuals.

6. 06

   Review a hypothetical messy Python pull request and correct it with comments.

   Tests code review skills: spotting naming and style issues, typos, async misuse and wrong Mistral API usage.

7. 07

   Given GPU clusters (for example [10, 20]) and a daily list of Usage objects, write a function that outputs how many GPUs are left each day, with GPUs returned at the end of each day.

   Tests array simulation with per-day capacity bookkeeping and end-of-day returns.

8. 08

   If a new workload can switch between clusters, what's the maximum number of GPUs it could get?

   Tests a per-day greedy maximum across clusters, summed over all days.

9. 09

   If the workload from the previous question can only switch clusters K times, what's the maximum number of GPUs it can get?

   Tests dynamic programming over days and cluster choices with at most K switches.

10. 10

    Implement a parser to compute the mathematical result of a string containing numbers and basic operators like + and * (without parentheses).

    Tests expression parsing with operator precedence for plus and multiply and no parentheses.

11. 11

    Solve a series of progressive coding challenges focused on efficient lookups and deduplication using arrays and sets.

    Tests hash set lookups and deduplication, with progressively tighter efficiency requirements.

12. 12

    Given a PyTorch tensor of point coordinates and a tensor of center coordinates, find the closest center for each point without materializing the full N x K x D distance tensor.

    Tests vectorized PyTorch with broadcasting and argmin while avoiding the full N x K x D tensor.

13. 13

    In a Python pair-programming session, combine a third-party API with the Mistral API to perform retrieval.

    Tests Python API integration: calling a third-party retrieval API and feeding results into the Mistral API context.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Design a synchronous inference API that converts a 1000-page PDF to Markdown as fast as possible, given a CPU-heavy page-splitting step, a GPU-heavy OCR engine and a memory-heavy step.

   Tests latency-focused system design by parallelizing CPU, GPU and memory-heavy stages across 1000 pages.

2. 02

   If there are many requests and results can be returned asynchronously, how would you design the PDF-to-Markdown API instead?

   Tests moving to an asynchronous design with queues and workers for high request volume.

3. 03

   Design a scalable Retrieval-Augmented Generation (RAG) system from scratch, detailing how you would manage cost, throughput, and latency.

   Tests RAG system design under cost, throughput and latency constraints.

4. 04

   Walk through the architecture of a low-latency RAG pipeline.

   Tests low-latency RAG architecture: retrieval, caching and serving choices.

5. 05

   Walk through the architecture of a high-throughput LLM inference engine, outlining your strategies for prompt caching, dynamic batching, and context window management.

   Tests LLM serving design: prompt caching, dynamic batching and context window management.

6. 06

   Design a high-throughput API to serve a Mistral model to millions of users.

   Tests high-throughput model serving design: scaling, latency and caching for millions of users.

7. 07

   Explain how you would implement and manage complex agentic workflows using frameworks like LangGraph or custom state machines.

   Tests agent orchestration design with a graph framework or custom state machines.

8. 08

   Build and discuss an LLM-powered retrieval-augmented agent that calls the Mistral API: ingest and chunk a small corpus, embed and index it, retrieve top-k passages, and invoke chat completions with streaming.

   Tests building a RAG agent: chunking, embeddings, top-k retrieval, streaming chat calls and error handling.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   How does KV caching improve inference speed?

   Tests the KV cache mechanism: reusing stored keys and values to avoid recomputing past tokens, and its memory cost.

2. 02

   What is MHA, GQA, KV-cache?

   Tests attention variants (multi-head versus grouped-query) and how the KV cache interacts with them.

3. 03

   Describe what a RAG is.

   Tests retrieval-augmented generation basics: retrieving passages and adding them to the model context.

4. 04

   If you have a 2000-page PDF knowledge base that does not fit in the context window, would you rather fine-tune a model or set up a RAG?

   Tests the RAG versus fine-tuning trade-off for a knowledge base too large for the context window.

5. 05

   Explain Mixture of Experts and expert parallelism.

   Tests mixture-of-experts routing and how expert parallelism spreads experts across devices.

6. 06

   Compare tensor parallelism and pipeline parallelism.

   Tests tensor parallelism (splitting layers across devices) versus pipeline parallelism (splitting stages).

7. 07

   Compare RMSNorm and LayerNorm in transformer blocks.

   Tests normalization choices in transformer blocks, such as RMSNorm skipping mean centering versus LayerNorm.

8. 08

   Explain Adam, AdamW, and decoupled weight decay.

   Tests optimizer mechanics: Adam moments and why decoupled weight decay differs from an L2 penalty under Adam.

9. 09

   Explain the forward and reverse processes of diffusion models.

   Tests diffusion basics: the noising forward process and the learned denoising reverse process.

10. 10

    Compare reward-model RLHF and Direct Preference Optimization.

    Tests preference training: a reward model plus policy optimization versus direct optimization on preference pairs.

11. 11

    How would you keep GPUs fed with an efficient data-preparation pipeline?

    Tests input pipeline throughput, such as parallel loading and prefetching, so training GPUs do not sit idle.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Why Mistral rather than another lab, including the US-based frontier labs?

2. 02

   Tell me about a time you moved a research idea into production.

3. 03

   Tell me about a project that started as a vague request. How did you structure it?

4. 04

   Describe the largest thing you have shipped with essentially no supervision.

5. 05

   Tell me about the fastest you have taken something from idea to production.

6. 06

   Tell me about an experiment you killed quickly. What told you?

7. 07

   What have you built or deployed with open-weight models?

8. 08

   In a live technical screen where AI coding assistants are allowed, how do you use them responsibly?