Everything you need to prepare for OpenTalent's five-stage screening -- and for senior AI engineering interviews at frontier labs. Written by engineers who've actually run the panels, kept short on theory, long on what gets asked.
// Featured this month
If you're reading one guide before your next loop, make it one of these.
A week-by-week prep schedule for the five-stage screen -- what to read, what to build, what to practice, and the mistakes that knock most people out at stage two.
The actual question bank -- from transformer internals to "tell me about a time you shipped something you weren't sure of."
// Prep by screening stage
Less than 3% of applicants make it through. Each stage has its own preparation. Click through to the dedicated playbook.
// All guides
Filter by topic above, or search by keyword. Each guide is updated quarterly as the frontier moves.
What "structured problem solving" actually means in the screen -- and how panelists score what they hear.
Live-coding and live-debug sessions reward narration more than speed. The four-beat structure to practice.
The brief is deliberately open-ended. Three rules for narrowing it without making it too small.
You'll get something wrong. Here's how the people who get offers respond -- and what kills the offer.
Engagement reviews, the bar for renewal, and how engineers turn one matched gig into a long-term portfolio.
Attention math, KV caching, MoE routing, and the gotchas panels probe -- with diagrams and worked examples.
SFT vs. preference tuning vs. RL -- when each is the right answer, and what's outdated in 2026.
Chinchilla, compute-optimal, and the open questions panels love. How to reason about it without a calculator.
A senior-engineer take on benchmark design, contamination, and how to defend an eval to a skeptical panel.
How to whiteboard a tool-using agent in 45 minutes -- planner, memory, executor, guardrails, and the trade-offs panels actually grade on.
Schema drift, argument hallucination, looping. The catalog that panel questions cycle through.
The current state of agent planning, where the patterns break, and what production teams quietly use instead.
Verifier engineering, sparse vs. dense rewards, and how to defend your choices when the panel pushes back.
State, action, reward, reset. The 30-minute version that gets you to a working spec -- and the pitfalls that don't.
DP, TP, PP, ZeRO, FSDP -- what panels expect you to know cold, and the trade-off questions to be ready for.
Speculative decoding, paged attention, batching strategies. What to mention first, what to skip.
Kueue, Slurm, Ray -- and the production AI workload questions panels are now asking junior infra engineers.
Image-text alignment, video grounding, and the embodied-AI questions hiring teams are asking in 2026.
OCR, layout parsing, structured extraction. A whiteboard loop you'll see at every applied-AI shop.
Chunking, hybrid retrieval, re-rankers, eval. The senior-AI-engineer version of the system-design interview.
A behavioural-loop favourite. The framework panels actually score the answer against.
Past the "few-shot examples" stage -- what depth panels want when they ask about prompting now.
// Curated study tracks
A four-week sequenced reading list for each common interview profile. Built so the harder material is reached when you're ready for it.
// Track 01
For engineers interviewing for foundation-model roles at frontier labs. Heavy on architectures, scaling, post-training, and evals.
// Track 02
For builders interviewing for production agent roles. Planner, memory, tool-use, and the system-design loop in detail.
// Track 03
For engineers interviewing for training-platform, inference, and observability roles. Distributed systems with an AI lens.
// Track 04
For full-stack and product-focused engineers shipping AI features. RAG, prompting, evals, and the behavioural loop.
// Common questions
If your question isn't here, write to hello@opentalent.in -- we read everything.
No. The guides are free and public. They're calibrated against OpenTalent's screening, but the questions and frameworks transfer cleanly to senior AI engineering interviews at frontier labs, AI-native startups, and most Fortune-500 AI teams.
Every guide is reviewed quarterly. We update for material that has shifted -- new architectures, new eval norms, new agent patterns. The "Updated" date on the top of each guide is the last review. If something looks out of date, write to us. We'd rather hear about it.
If you have less than 10 days, skip the four-week tracks and start with the featured playbook at the top of this page. Then read the two stage guides that cover your remaining rounds. Skim the 50-questions cheat sheet on the morning of.
Yes. Every guide is co-written by an engineer who has either run the OpenTalent screening for that stage, or has hired into the equivalent role at a frontier lab. We don't publish guides written by non-practitioners.
Not yet on this page. We have rubrics and panel-design templates we share with active client teams. Write to hello@opentalent.in with your context and we'll share the relevant ones.
If you're an active OpenTalent network member who has hired senior AI engineers, yes -- pitch us at hello@opentalent.in. Honoraria included for accepted guides.
Apply to join OpenTalent. Less than 3% of applicants make it -- and the prep starts the moment you submit.