Sr Staff ML ยท Frontier-lab Interview Prep

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A complete prep system for senior-level ML engineers targeting frontier AI labs (Anthropic, OpenAI, DeepMind, Thinking Machines, and adjacent). 25+ deep chapters, drills, flashcards, mock interviews, and a curated company shortlist. Press โŒ˜K anywhere to jump.

๐Ÿ‘ค Click the avatar (top right) to create your own profile โ€” progress, chapter checkmarks, and flashcards stay separate per person.

โ–ถ Adaptive study plan Course โ†’ test โ†’ verdict ยท 27 modules ๐Ÿƒ Flashcards 164 cards ยท spaced repetition โ—‰ Mock interview Random prompt + timer ๐Ÿ“Œ Study deeper Your flagged cards aggregated
โ†ฉ Resume Last page โš„ Random topic Drop into a chapter โ€นโ€บ Coding patterns DP ยท backtracking ยท threads โ–ฆ 12-week roadmap Big-picture plan

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The plan in one screen

1. Bootstrap Weeks 1โ€“2

Rebuild coding muscle. Refresh DL fundamentals. Diagnose weak spots honestly.

  • Daily 2 LeetCode mediums + 1 hard / week
  • Re-derive backprop, attention, RoPE on paper
  • One mock interview to baseline

2. Depth Weeks 3โ€“8

Go deep where it matters: LLM training, distributed systems (the common weak spot), and ML system design.

  • Read papers + write notes on this site
  • Drill ML coding (attention, k-means, BPE, sampling)
  • One full ML system design / week

3. Apply + iterate Weeks 9โ€“12

Open the funnel. Tier-A first, top tier last. Use early loops as practice.

  • Apply 5 companies / week
  • Mock interviews with peers + interviewing.io
  • Negotiate using competing offers
Study tools
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Adaptive study plan

27 modules ยท course โ†’ test โ†’ verdict

Interleaved curriculum across all tracks. Read a chapter, take its test, get a verdict. Mastered modules schedule themselves for review (14 โ†’ 30 โ†’ 60 โ†’ 120 โ†’ 240 days).

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Flashcards

164 cards ยท 15 decks ยท SRS

Tap-to-flip, swipe-to-next, Hard/Medium/Easy spaced repetition. "Learn more" links + flag for deeper study on every card.

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Study deeper

Your flagged weak spots

Every card you flagged (๐Ÿ“Œ) appears here, grouped by topic, each linked to the full chapter. Your personal weak-spots course.

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Mock interviews

Curated mocks ยท timer ยท 50+ prompts

Random-mock button + timer + filters. Pre-built loops to simulate Anthropic / OpenAI / Pinterest onsites end-to-end.

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Content pillars
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Coding

Patterns, deep dives, ML coding, OS & networking

Anthropic does take-homes + paired coding. OpenAI uses CoderPad. Most labs ask ML coding (attention, sampling, BPE) and build-it systems problems more than LeetCode hards.

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ML systems โ€” zero to expert

28 chapters ยท 7 parts ยท the centerpiece course

From "what is an ML system" to KV-cache arithmetic, 3D parallelism, RecSys funnels and RAG โ€” every mechanism taught by showing what breaks without it. Pair with the challenges page.

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ML systems challenges

Design + debug war rooms ยท capacity drills

Attempt-first practice: design under hard constraints, debug from real telemetry, estimate capacity with napkin math, grade yourself on the junior/senior/staff rubric.

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Probability & statistics

Counting โ†’ CLT โ†’ MLE/MAP โ†’ A/B testing

The full prob/stats stack in textbook order: tiny numeric examples first, every formula glossed, puzzle patterns and the rapid-fire bank for interview craft.

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Mini projects & notebooks

8 notebooks ยท TODOs + asserts ยท solutions gated

Build a two-tower, a transformer block, a BPE tokenizer, a KV cache, a mini-RAG, a calibration lab, an ANN index, and an on-call debugging sim โ€” concepts stick when you build them.

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ML theory fundamentals

Bias/variance, ReLU, MLE, GLMs, calibration

The "tricky" questions that signal deep understanding. 50+ readiness questions. Cover before any loop where ML fundamentals could be probed.

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LLM training & RLHF

Pretraining, SFT, DPO/GRPO, reasoning

Frontier-lab interviews probe scaling laws, data mixing, RLHF tradeoffs, reasoning-model training. Be ready to design a Llama-scale run end-to-end.

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LLM inference

vLLM, speculative decoding, paged attention

OpenAI / Anthropic / TML serving teams ask deep inference Qs. Know FlashAttention, KV cache layouts, tensor parallel, throughput vs TTFT.

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ML system design

30 chapters ยท feeds, search, ads, RAG + infra primers

The design-interview playbook: the 8-step framework, full worked designs (feed, video watch-next, search, a 5-chapter ads deep-dive, RAG), and the infra vocabulary that wins the room.

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Recommender systems

Two-tower โ†’ DLRM โ†’ SASRec โ†’ TIGER โ†’ RecGPT

The full RecSys canon with depth interviewers probe: calibration, exposure bias, in-batch vs hard negatives, MMoE, sequence models, generative recsys.

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Distributed systems

Common weak spot โ€” invest heavily here

CAP, consensus, sharding, replication, Kafka, Spanner, Dynamo. Read DDIA cover-to-cover. Then 6.824 Raft labs if time allows.

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Concurrency

Threads, GIL, async, lock-free, memory models

Especially for OpenAI / Cursor / TML where infra interviews are real. Know Python GIL, asyncio, multi-threading hazards, basic lock-free patterns.

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Mech interp

Anthropic-critical

Induction heads, sparse autoencoders, residual stream, activation patching. Every Anthropic loop probes "what's a circuit?" โ€” read this before any onsite.

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Reasoning models

o1 / o3 / R1 era

RLVR, GRPO, process reward models, MCTS, inference-time scaling. The 2025 frontier. DeepSeek R1's recipe is the open canon.

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Multimodal & diffusion

For World Labs / BFL / Luma / PI

DDPM, flow matching, rectified flow, DiT, MM-DiT. CLIP vs SigLIP. VLA for robotics. Required for any vision/multimodal-team role.

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Evals reference

Cheat sheet of every benchmark

MMLU vs MMLU-Pro vs GPQA-Diamond. SWE-Bench Verified vs Lite. What's saturated, what's contaminated, current SOTA.

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Company shortlist

Bay Area ยท ML-focused ยท Sr/Staff

Filtered tier list of ~50 targets. Wishlist: Anthropic, OpenAI, Thinking Machines. Tier A: DeepMind, Databricks, Pinterest, Anduril, World Labs, PI.

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Daily ritual

Every day
  • 1 LeetCode (rotate pattern: graph / DP / heap / interval)
  • 1 hour deep-dive on a topic from this site (read + take notes here)
  • 30 min ML coding (implement something from scratch)
  • Read 1 recent paper (arxiv-sanity, Lilian Weng, HF daily)
  • Update application tracker if any movement

Weekly ritual

Every week
  • 1 mock interview (Pramp / interviewing.io / friend)
  • 1 ML system design walkthrough (whiteboard, time-boxed)
  • 1 distributed systems chapter (DDIA) + write notes
  • Reach out to 2 connections at target companies (ref pipeline)
  • Friday review: what's stuck, what to drill next week
North star
If you haven't interviewed in years, the first 2 weeks will feel humbling โ€” that's normal. Don't open with your dream company: interview at a Tier-B first to recalibrate, and treat the early loops as paid mocks.
Living document ยท Last refresh 2026-06-10 ยท Press โŒ˜K to search anything ยท Edit any page directly