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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 โK anywhere to jump.
๐ค Click the avatar (top right) to create your own profile โ progress, chapter checkmarks, and flashcards stay separate per person.
Progress across pillars
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The plan in one screen
1. Bootstrap
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
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
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
Mock interviews
Random-mock button + timer + filters. Pre-built loops to simulate Anthropic / OpenAI / Pinterest onsites end-to-end.
โ โนโบCoding
Anthropic does take-homes + paired coding. OpenAI uses CoderPad. Most labs ask ML coding (attention, sampling, BPE) more than LeetCode hards.
โ ฮธML theory fundamentals
The "tricky" questions that signal deep understanding. 50+ readiness questions. Cover before any loop where ML fundamentals could be probed.
โ โปLLM training & RLHF
Frontier-lab interviews probe scaling laws, data mixing, RLHF tradeoffs, reasoning-model training. Be ready to design a Llama-scale run end-to-end.
โ โซLLM inference
OpenAI / Anthropic / TML serving teams ask deep inference Qs. Know FlashAttention, KV cache layouts, tensor parallel, throughput vs TTFT.
โ โคML system design
Your strongest area. Lean on RecSys + ranking experience but extend to LLM serving, RAG, and multi-modal at scale.
โ โRecommender systems
Your bread and butter. Show depth: calibration, exposure bias, in-batch vs hard negatives, MMoE, sequence models, generative recsys.
โ โฌDistributed systems
CAP, consensus, sharding, replication, Kafka, Spanner, Dynamo. Read DDIA cover-to-cover. Then 6.824 Raft labs if time allows.
โ โConcurrency
Especially for OpenAI / Cursor / TML where infra interviews are real. Know Python GIL, asyncio, multi-threading hazards, basic lock-free patterns.
โ โMech interp
Induction heads, sparse autoencoders, residual stream, activation patching. Every Anthropic loop probes "what's a circuit?" โ read this before any onsite.
โ โฌReasoning models
RLVR, GRPO, process reward models, MCTS, inference-time scaling. The 2025 frontier. DeepSeek R1's recipe is the open canon.
โ โMultimodal & diffusion
DDPM, flow matching, rectified flow, DiT, MM-DiT. CLIP vs SigLIP. VLA for robotics. Required for any vision/multimodal-team role.
โ โEvals reference
MMLU vs MMLU-Pro vs GPQA-Diamond. SWE-Bench Verified vs Lite. What's saturated, what's contaminated, current SOTA.
โ โCompany shortlist
Filtered tier list of ~50 targets. Wishlist: Anthropic, OpenAI, Thinking Machines. Tier A: DeepMind, Databricks, Pinterest, Anduril, World Labs, PI.
โDaily ritual
- 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
- 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