Sources and attribution

English · Русский · AI Interview Atlas › Reference

Questions marked † that come from AI Engineering Interview Questions Company Wise (Outcome School) are paraphrased and attributed to it; that compilation is licensed under Apache-2.0.

Company sources

• How we hire · Adyen · retrieved 2026-09-26
• Interview tips from our Global Recruitment team · Adyen · published 2025-09-01 · retrieved 2026-09-26
• TPM Interview Prep · Amazon Jobs · retrieved 2026-10-05. Undated official preparation page for general TPM interviews: technical phone screen, writing assessment and interview loop. It does not establish an AI-specific loop or supply the atlas editorial questions.
• Careers · Anthropic · retrieved 2026-09-26
• Designing AI resistant technical evaluations · Anthropic · published 2026-01-21 · retrieved 2026-09-29. Historical account of performance-engineering take-homes introduced in early 2024 and subsequently retired or replaced; not a description of the current interview.
• Guidance on Candidates' AI Usage · Anthropic · published 2025-07-10 · retrieved 2026-09-26
• Atlassian engineering interview handbook · Atlassian · retrieved 2026-09-26
• Atlassian product interview handbook · Atlassian · retrieved 2026-09-26
• Собеседование DS-специалиста в Авито 13 октября: ML system design · Avito Tech · published 2025-10-07 · retrieved 2026-09-29. A public mock interview on 13 October 2025, ahead of a DS Weekend Offer on 18–19 October; the year comes from the page metadata.
• AI Interview Success: An Interviewer's Inside Guide · Canva Engineering Blog · published 2025-10-20 · retrieved 2026-09-30. A Canva interviewer, Karl Hornlund, describes what the AI-assisted coding round assesses and names the patterns that score badly, including delegating too much and accepting generated output without review.
• Yes, You Can Use AI in Our Interviews. In fact, we insist · Canva Engineering Blog · published 2025-06-11 · retrieved 2026-09-30. Canva's own announcement, by Simon Newton, that backend, machine learning and frontend candidates are expected to use their preferred AI tools in an AI-assisted coding interview that replaced the computer-science fundamentals screen.
• Director, Engineering · GitLab Handbook · retrieved 2026-10-05. Public role-family page with an actual Hiring Process section; last modified 2026-03-12. Supports general Engineering Director responsibilities and interview stages, not reported AI interview questions.
• Product Management - Leadership · GitLab Handbook · retrieved 2026-10-05. Role-family responsibilities and a Director hiring process were readable. General product leadership scope; no AI-specific interview questions. Publication date not established.
• Preparing for Your Initial Interview (software engineering managers) · Meta Careers · retrieved 2026-09-26
• Preparing for Your Interviews at Meta (technical program managers) · Meta Careers · retrieved 2026-09-26
• How we hire · Microsoft Careers · retrieved 2026-09-26
• Technical interviewing · Microsoft Careers · retrieved 2026-09-26
• Demystifying the Backend Engineering interview process · Monzo · published 2025-03-01 · retrieved 2026-09-26
• Demystifying the Senior Staff+ Engineering interview process · Monzo · published 2025-05-02 · retrieved 2026-09-26
• Product Management at Monzo: The interview process · Monzo · published 2022-10-04 · retrieved 2026-09-26
• How We Hire · NVIDIA · retrieved 2026-09-26
• Getting Hired · Palantir Careers · retrieved 2026-09-29. Includes guides to the competencies interviews assess, such as working inside existing systems and navigating open-ended questions.
• Working Inside Existing Systems · Palantir Careers · retrieved 2026-09-29
• Forward Deployed Engineering · Ramp Builders · published 2025-08-05 · retrieved 2026-09-29
• Как подготовиться к техническому интервью Senior NLP GigaChat · Sber, developers.sber.ru · retrieved 2026-09-30. Sber's own preparation page for the senior NLP role on GigaChat. It names each section and the topics it covers, including supervised finetuning, retrieval-augmented generation and RLHF, but states no questions and shows no publication or update date.
• Как проходят интервью · SberDevices, страница для ML-разработчиков · retrieved 2026-09-29
• The AI-native interview · Sierra · published 2026-04-22 · retrieved 2026-09-29
• Start Your Journey · Spotify · retrieved 2026-09-26
• How we hire · Uber · retrieved 2026-09-26
• Navigating our engineering interview process: coding · Uber · published 2021-07-27 · retrieved 2026-09-26
• Как мы переделали структуру собеседований, и что из этого вышло · Авито, блог на Хабре · published 2019-03-20 · retrieved 2026-09-26
• Материалы для подготовки к собеседованию на позицию Data Scientist. Часть 3: Специализированное машинное обучение · МегаФон, блог на Хабре · published 2024-04-19 · retrieved 2026-09-29
• Материалы для подготовки к собеседованию на позицию Data Scientist. Часть 4: Дизайн систем машинного обучения · МегаФон, блог на Хабре · published 2024-06-26 · retrieved 2026-09-29
• Секция по ML · Т-Банк (Tinkoff), репозиторий career на GitHub · published 2022-08-01 · retrieved 2026-09-29. The repository was archived in 2023; the section text dates from 2022-08-01.
• Секция по дизайну ML систем · Т-Банк (Tinkoff), репозиторий career на GitHub · published 2022-08-01 · retrieved 2026-09-29. The repository was archived in 2023; the section text dates from 2022-08-01.
• Бесполезные курсы и помешательство на GPTs: как мы искали prompt-инженеров · Точка, блог на Хабре · published 2024-10-31 · retrieved 2026-09-29
• Как нанимаем, грейдируем и решаем типовые задачи prompt-инженеров в Точке · Точка, блог на Хабре · published 2025-06-26 · retrieved 2026-09-29
• «Важно не только занять должность, но и работать на ней»: советы перед ИТ-собеседованиями · Т—Ж (издание Т-Банка), интервьюеры Т-Банка · published 2023-06-07 · retrieved 2026-09-26
• Алгоритмические собеседования в Яндексе: как подготовиться и чего ожидать · Яндекс Образование · published 2024-12-19 · retrieved 2026-09-26
• Секция на проверку базовых технических навыков ML-инженеров · Яндекс Образование · published 2025-11-30 · retrieved 2026-09-29. A recorded demonstration of the section.
• Как проходят архитектурные секции собеседования в Яндексе: практика дизайна распределённых систем · Яндекс, блог на Хабре · published 2021-06-24 · retrieved 2026-09-26

Candidate reports

• Anthropic Engineering Manager Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-09-26. Relative labels on retrieval: interview six months earlier, submission five months earlier. These are approximate, separate dates.
• Anthropic Staff Software Engineer, Infrastructure Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-09-26. Relative labels on retrieval: interview about one year earlier, submission five months earlier. These are approximate, separate dates.
• Cursor Software Engineer (New Grad) Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-09-29. A new-graduate candidate; the page dates the interview only relatively, about a year before retrieval.
• Google Engineering Manager (L6) Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-10-05. The original URL redirected to the same participant report on Aced when read on 2026-10-05. Low performance and disruptive high performance are hypothetical prompts; the organisational-change prompt asks about actual high-performer attrition. This report concerns general engineering management, not a specialist AI loop.
• Meta Engineering Manager (M1) Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-09-26. The existing participant report was unavailable on a new access attempt on 2026-10-05, including its Aced redirect. Keep the last successful retrieval date; this attempt does not freshly verify the cross-team delivery question.
• Meta Product Manager, AI Interview Experience (level unverified) · Aced (formerly Exponent), candidate report · retrieved 2026-09-26. Relative labels: interview eight months earlier, submission four months earlier. The retained metadata says L4, conflicting with the earlier L5 title; level is unverified.
• Sierra AI Agent Engineer Interview Experience · Aced (formerly Exponent), candidate report · retrieved 2026-09-29. The page dates the interview only relatively, about a year before retrieval, so before Sierra's April 2026 redesign.
• I failed my Anthropic interview and came to tell you all about it so you don't have to · Fail. Learn. Repeat. · published 2025-02-12 · retrieved 2026-09-29. A Research Fellowship candidate's account; the author added an update in September 2025.
• So You Want to Hire a Forward Deployed Engineer · First Round Review · published 2026-02-24 · retrieved 2026-09-29. Hiring-side accounts, including a former Palantir FDE who led FDE recruiting there and a former head of legal engineering at Ironclad.
• How I Prepared for and Got a Spotify ML Engineer Offer · Njord tech blog, post by a candidate · published 2025-08-30 · retrieved 2026-09-30. A first-hand account of a machine-learning engineer loop at Spotify that ended in an offer, written under the pseudonym Njord. The author states that no specific interview questions will be shared and gives no reason for withholding them, so this source supports loop stages only.
• Adyen Technical Test · Reddit, r/cscareerquestionsEU, applicant report · retrieved 2026-09-26. Historical test invitation for a Java software-engineering role. Exact interview date is not supplied; not evidence of a current universal process.
• Anthropic AI Prompt Engineer Interview Experience - San Francisco, California · Taro, candidate report · retrieved 2026-09-29. The interview is dated September 2023; the role title may no longer exist.
• Anthropic Product Engineer Interview Experience - New York, New York · Taro, candidate report · retrieved 2026-09-29. The interview is dated June 2025.
• Anthropic Solutions Architect Interview Experience - United Kingdom · Taro, candidate report · retrieved 2026-09-29. The interview is dated May 2025.
• Palantir Forward Deployed Software Engineer Interview Experience - Denver, Colorado · Taro, candidate report · retrieved 2026-09-29. The interview is dated March 2025; the candidate accepted an offer.
• Palantir Forward Deployed Software Engineer Interview Experience - New York, New York · Taro, candidate report · retrieved 2026-09-29. The interview is dated 2025-08-15.
• Palantir Forward Deployed Software Engineer Interview Experience - United States · Taro, candidate report · retrieved 2026-09-29. The interview is dated November 2025.
• Как принимает на работу Авито и почему это лучшее, что я видела на рынке? (часть 2) · vc.ru, пост кандидата · published 2025-06-25 · retrieved 2026-09-26
• Как я проходил собеседование в SberDevices · Т—Ж, рассказ кандидата · published 2023-05-29 · retrieved 2026-09-29
• Как я отказался от миллионных RSU или опыт собеседования в Ozon · Хабр, пост кандидата · published 2022-02-03 · retrieved 2026-09-26
• Как я собеседовался в Ozon, Т-Банк, Mindbox и другие крупные компании · Хабр, пост кандидата · published 2025-07-09 · retrieved 2026-09-26
• Как я собеседую менеджеров AI-продуктов для крупного Enterprise · Хабр, рассказ интервьюера · published 2026-05-23 · retrieved 2026-09-29. An interviewer's first-hand account; the company is not named.
• Что теперь нужно знать современному LLM-инженеру · Хабр, рассказ кандидата · published 2026-08-25 · retrieved 2026-09-29. An engineer's account after a round of job interviews at Russian and foreign companies, which are not named.

Preparation guides

• Amazon AI Product Manager Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 11 days earlier; publication date unknown.
• Anthropic Forward Deployed Engineer (FDE) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-29
• Anthropic Software Engineer (SWE) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 12 days earlier; this relative update label is not a publication date.
• Cognition Forward Deployed Engineer Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-29
• Gemini Product Manager Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated four months earlier; publication date unknown.
• Get a Job at Databricks: Interview Process and Top Questions · Aced (formerly Exponent) · retrieved 2026-09-26. The retained page digest says published two years earlier, approximately 2024; the exact date is unknown. Treat the process as historical.
• Get a Job at Stripe: Interview Process and Top Questions · Aced (formerly Exponent) · retrieved 2026-10-05. Reopened on 2026-10-05 through the original URL, which redirected to Aced. The engineering-management section discusses the hiring bar and the candidate’s last strategic hire. This is a preparation guide, not a participant report; the atlas paraphrase was narrowed accordingly.
• Google Engineering Manager (EM) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 10 days earlier; publication date unknown.
• Interviewing at Stripe · Aced (formerly Exponent) · retrieved 2026-09-26. Community-submitted questions, including a strategy question on an internal AI platform with metric ownership.
• LangChain Software Engineer Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-29
• Microsoft AI Product Manager Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 15 days earlier; publication date unknown.
• OpenAI Forward Deployed Engineer (FDE) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-29
• OpenAI Product Manager (PM) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 18 days earlier; publication date unknown.
• Palantir Deployment Strategist Interview Guide · Aced (formerly Exponent) · published 2026-08-20 · retrieved 2026-09-29
• Palantir Forward Deployed Engineer (FDE) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated 19 days earlier; publication date unknown.
• Perplexity AI Product Manager (PM) Interview Guide · Aced (formerly Exponent) · retrieved 2026-09-26. On retrieval, the page said updated five months earlier; publication date unknown.
• Stripe SWE Interview: Bug Squash Guide · Coditioning · published 2026-06-25 · retrieved 2026-09-26
• The Palantir Decomposition Interview, Explained · FDEInterviews.com · retrieved 2026-09-29. The page says it was updated on 12 August 2026; its example prompts are suggestions for practice, not reported questions.
• Anthropic's Interview Process & Questions · interviewing.io · retrieved 2026-09-26. Based partly on engineer conversations in 2026; no publication or update date was stated.
• Nvidia’s Interview Process & Questions · interviewing.io · retrieved 2026-09-26. Based on conversations with engineers in 2024; that is the evidence period, not an established publication date.
• OpenAI's Interview Process & Questions · interviewing.io · retrieved 2026-09-26
• Spotify’s Interview Process & Questions · interviewing.io · retrieved 2026-09-26. Undated guide; conflicting team-matching descriptions are not adopted here.
• Uber’s Interview Process & Questions · interviewing.io · retrieved 2026-09-26

Compilations

• AI Engineering Interview Questions Company Wise · Outcome School · published 2026-09-19 · retrieved 2026-09-26 · license Apache-2.0. A compilation of publicly reported questions without a source or date per question; its company tags are shown as secondary (†).

Reference material

• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes · Agarwal et al., arXiv · published 2023-06 · retrieved 2026-10-02
• Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve · Agrawal et al., arXiv · published 2024-03 · retrieved 2026-10-02
• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints · Ainslie et al., arXiv · published 2023-05 · retrieved 2026-09-26
• Flamingo: a Visual Language Model for Few-Shot Learning · Alayrac et al., arXiv · published 2022-04 · retrieved 2026-09-26
• Moshi: a speech-text foundation model for real-time dialogue · Alexandre Defossez et al., arXiv · published 2024-09-17 · retrieved 2026-10-05
• Leadership Principles · Amazon Jobs · retrieved 2026-09-26
• Cost Optimization Pillar — AWS Well-Architected Framework · Amazon Web Services · retrieved 2026-10-05. Primary cloud cost framework: financial management, expenditure awareness, resource selection and demand. It supplies categories to investigate, not case-specific prices or savings estimates.
• Speech marks · Amazon Web Services · retrieved 2026-10-05
• Concrete Problems in AI Safety · Amodei et al., arXiv · published 2016-06 · retrieved 2026-10-02
• Build an offline-first app · Android Developers · retrieved 2026-10-05. Primary architecture guidance on local and network data, synchronisation and conflicting writes. Reference for the field-catalogue case; no interview attribution.
• Anthropic's Responsible Scaling Policy · Anthropic · retrieved 2026-09-26
• Building Effective AI Agents · Anthropic · published 2024-12 · retrieved 2026-09-26
• Contextual Retrieval in AI Systems · Anthropic · published 2024-09 · retrieved 2026-09-26
• Demystifying evals for AI agents · Anthropic · retrieved 2026-09-26
• Effective context engineering for AI agents · Anthropic · retrieved 2026-09-26
• How we built our multi-agent research system · Anthropic · published 2025-06-13 · retrieved 2026-09-29. First-party account of a research product, including coordination and token costs. Its internal results are workload-specific; not evidence of interview questions.
• Structured outputs · Anthropic · retrieved 2026-10-05
• Writing effective tools for AI agents—using AI agents · Anthropic · retrieved 2026-09-26
• AP2 - Agent Payments Protocol Documentation · AP2 project · retrieved 2026-09-26
• Dependency Mapping · Atlassian Team Playbook · retrieved 2026-10-02
• Constitutional AI: Harmlessness from AI Feedback · Bai et al., arXiv · published 2022-12 · retrieved 2026-09-26
• LoRA Learns Less and Forgets Less · Biderman et al., arXiv · published 2024-05 · retrieved 2026-10-02
• Supervisory Letter SR 11-7 on guidance on Model Risk Management · Board of Governors of the Federal Reserve System · published 2011-04-04 · retrieved 2026-09-26
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control · Brohan et al., arXiv · published 2023-07 · retrieved 2026-10-02
• Extending Context Window of Large Language Models via Positional Interpolation · Chen et al., arXiv · published 2023-06 · retrieved 2026-10-02
• Reasoning Models Don't Always Say What They Think · Chen et al., arXiv · published 2025-05-08 · retrieved 2026-09-29
• PaLM: Scaling Language Modeling with Pathways · Chowdhery et al., arXiv · published 2022-04 · retrieved 2026-10-02
• Prompt caching · Claude Platform Docs · retrieved 2026-09-26
• Tool use with Claude · Claude Platform Docs · retrieved 2026-09-26
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness · Dao et al., arXiv · published 2022-05 · retrieved 2026-09-26
• The Tail at Scale · Dean and Barroso, Google Research · published 2013 · retrieved 2026-09-26
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (v1) · DeepSeek-AI, arXiv · published 2025-01-22 · retrieved 2026-09-29
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model · DeepSeek-AI, arXiv · published 2024-05 · retrieved 2026-09-26
• QLoRA: Efficient Finetuning of Quantized LLMs · Dettmers et al., arXiv · published 2023-05 · retrieved 2026-09-26
• Aliases · Elastic · retrieved 2026-10-05
• Patterns for Building LLM-based Systems & Products · Eugene Yan · published 2023-07 · retrieved 2026-09-26
• AI Act · European Commission · retrieved 2026-09-26
• Faiss indexes · Faiss maintainers · retrieved 2026-09-28. Primary implementation documentation for HNSW, IVF and product quantisation; reference reading, not interview evidence.
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity · Fedus et al., arXiv · published 2021-01 · retrieved 2026-09-26
• Insider Trading Detection: FINRA’s Vital Role in Ensuring Market Integrity · FINRA · published 2024-05-14 · retrieved 2026-10-05. FINRA staff describe investigation inputs and suspicious trading detection in a published discussion. Domain reading for decomposition; no claim that these are employer interview questions.
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers · Frantar et al., arXiv · published 2022-10 · retrieved 2026-10-02
• Precise Zero-Shot Dense Retrieval without Relevance Labels · Gao et al., arXiv · published 2022-12 · retrieved 2026-09-26
• Scaling Laws for Reward Model Overoptimization · Gao, Schulman and Hilton, arXiv · published 2022-10-19 · retrieved 2026-09-29. Studies overoptimisation with a synthetic gold-standard reward model; technical reading, not interview evidence or a direct measurement of human preferences.
• Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? · Gekhman et al., arXiv · published 2024-05 · retrieved 2026-10-02
• Communication · GitLab Handbook · retrieved 2026-10-05. Scoped reading: decision ownership, explaining a decision after consultation, and internal alignment before external commitments. This is one employer’s operating practice, not a universal interview rubric.
• Compensation at Gitlab · GitLab Handbook · retrieved 2026-10-05. Content last updated 2026-02-19. Primary explanation separating salary, variable pay and equity; reading for an honest discussion of mission and compensation tradeoffs. GitLab-specific terms do not describe another employer’s offer or interview.
• How to recruit UX Research participants · GitLab Handbook · retrieved 2026-10-02
• Organizational Change Management · GitLab Handbook · retrieved 2026-09-26
• AIP-158: Pagination · Google · retrieved 2026-10-05
• Postmortem Culture: Learning from Failure · Google · retrieved 2026-09-26
• Site Reliability Engineering · Google · published 2016 · retrieved 2026-09-26
• Rules of Machine Learning · Google for Developers · retrieved 2026-09-26
• User Needs + Defining Success · Google PAIR · retrieved 2026-10-05. Primary design guidance on user problems, the value of AI and success criteria. Undated chapter; reference reading, not interview evidence.
• [re:Work] Google's New Manager Training Slides · Google re:Work · retrieved 2026-09-26
• Data and AI Ethics Framework · Government Digital Service · retrieved 2026-10-05. Updated 2025-12-18. Primary public-sector framework covering accountability, affected people and ethical tradeoffs. Transfer the questions with context; this is not legal advice or employer interview evidence.
• How the alpha phase works · Government Digital Service · retrieved 2026-10-05. Primary public-service guidance on prototypes and testing risky assumptions. Method transfer to product cases; not employer interview evidence. Publication date not established in this check.
• Managing technical lock-in in the cloud · Government Digital Service and Central Digital and Data Office · published 2019-12-17 · retrieved 2026-10-05. Updated 2026-09-03. Primary guidance on balancing service value against switching cost and maintaining an exit strategy. Reference reading, not an AI interview report.
• Your AI Product Needs Evals · Hamel Husain · published 2024-03 · retrieved 2026-09-26
• Distilling the Knowledge in a Neural Network · Hinton et al., arXiv · published 2015-03 · retrieved 2026-10-02
• The Green Book (2026) · HM Treasury · retrieved 2026-10-05. 2026 edition, page updated 2026-02-05. Public-sector appraisal guidance used for option comparison and uncertainty; its social valuation and discount assumptions are not corporate finance defaults.
• Training Compute-Optimal Large Language Models · Hoffmann et al., arXiv · published 2022-03 · retrieved 2026-09-26
• The Curious Case of Neural Text Degeneration · Holtzman et al., arXiv · published 2019-04 · retrieved 2026-10-02
• RULER: What's the Real Context Size of Your Long-Context Language Models? · Hsieh et al., arXiv · published 2024-04 · retrieved 2026-10-02
• LoRA: Low-Rank Adaptation of Large Language Models · Hu et al., arXiv · published 2021-06 · retrieved 2026-09-26
• Optimizing inference · Hugging Face Transformers docs · retrieved 2026-09-26
• Deep Learning: Probability and Information Theory · Ian Goodfellow, Yoshua Bengio and Aaron Courville · published 2016 · retrieved 2026-10-05
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations · Inan et al., arXiv · published 2023-12 · retrieved 2026-10-02
• Design Docs at Google · Industrial Empathy · retrieved 2026-09-26
• Efficient Memory Management for Large Language Model Serving with PagedAttention · Kwon et al., arXiv · published 2023-09 · retrieved 2026-09-26
• Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows · Lei et al., arXiv · published 2024-11 · retrieved 2026-10-02
• The Power of Scale for Parameter-Efficient Prompt Tuning · Lester et al., arXiv · published 2021-04 · retrieved 2026-10-02
• Fast Inference from Transformers via Speculative Decoding · Leviathan et al., arXiv · published 2022-11 · retrieved 2026-09-26
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks · Lewis et al., arXiv · published 2020-05 · retrieved 2026-09-26
• Prefix-Tuning: Optimizing Continuous Prompts for Generation · Li et al., arXiv · published 2021-01 · retrieved 2026-10-02
• Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning · Liang et al., arXiv · published 2022-03 · retrieved 2026-10-02
• Let's Verify Step by Step · Lightman et al., arXiv · published 2023-05-31 · retrieved 2026-09-29
• Lost in the Middle: How Language Models Use Long Contexts · Liu et al., arXiv · published 2023-07 · retrieved 2026-09-26
• Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs · Malkov and Yashunin, arXiv · published 2016-03 · retrieved 2026-09-26
• A Holistic Approach to Undesired Content Detection in the Real World · Markov et al., arXiv · published 2022-08 · retrieved 2026-10-02
• Refactoring · Martin Fowler · retrieved 2026-10-02
• Team Topologies · Martin Fowler · published 2023-07-25 · retrieved 2026-10-02
• FP8 Formats for Deep Learning · Micikevicius et al., arXiv · published 2022-09 · retrieved 2026-10-02
• Architect multitenant solutions on Azure · Microsoft Learn · retrieved 2026-09-26
• Chunk and vectorize with the Document Layout skill · Microsoft Learn · retrieved 2026-10-05
• Compensating Transaction pattern · Microsoft Learn · retrieved 2026-10-02
• Document-level access control · Microsoft Learn · retrieved 2026-09-26
• How to use the Voice Live API · Microsoft Learn · retrieved 2026-10-05
• Improve recognition accuracy with phrase list · Microsoft Learn · retrieved 2026-10-05
• Interpret and improve model accuracy and confidence scores · Microsoft Learn · retrieved 2026-10-05
• Text to speech transparency note · Microsoft Learn · retrieved 2026-10-05
• Patterns of Trustworthy Experimentation: Pre-Experiment Stage · Microsoft Research · published 2020-07-31 · retrieved 2026-10-05. Primary experimentation guidance covering hypotheses, guardrails, randomisation units and contamination. Supports experiment design, not an employer interview claim.
• Model Context Protocol specification · Model Context Protocol · retrieved 2026-09-26
• Security Best Practices · Model Context Protocol · retrieved 2026-10-05
• The Third DIHARD Diarization Challenge · Neville Ryant et al., arXiv · published 2020-12-02 · retrieved 2026-10-05
• NHS service standard · NHS Digital Service Manual · retrieved 2026-10-05. Primary service-design standard for health and care services. Reading for whole patient journeys and service constraints; not a clinical decision protocol or interview report.
• AI Risk Management Framework · NIST · published 2023-01 · retrieved 2026-09-26
• Guidelines for Evaluating Differential Privacy Guarantees (SP 800-226) · NIST · published 2025-03-06 · retrieved 2026-10-02
• What We Learned from a Year of Building with LLMs (Part I) · O'Reilly Radar · published 2024-05 · retrieved 2026-09-26
• OSHA Technical Manual: Industrial Robot Systems and Industrial Robot System Safety · Occupational Safety and Health Administration · retrieved 2026-10-05. Industrial robot hazard-control guidance; a learning reference, not a complete safety case or certification for a learned policy.
• Age assurance duties under the Online Safety Act · Ofcom · retrieved 2026-10-05. UK-specific regulator guidance; requirements and applicability must be checked for the actual service and jurisdiction.
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models · Open X-Embodiment Collaboration, arXiv · published 2023-10 · retrieved 2026-10-02
• Evaluation best practices · OpenAI API documentation · retrieved 2026-09-29
• Reasoning models · OpenAI API documentation · retrieved 2026-09-29
• Proving Test Set Contamination in Black Box Language Models · Oren et al., arXiv · published 2023-10 · retrieved 2026-10-02
• Training language models to follow instructions with human feedback · Ouyang et al., arXiv · published 2022-03 · retrieved 2026-09-26
• OWASP Top 10 for LLM Applications · OWASP Gen AI Security Project · retrieved 2026-09-26
• YaRN: Efficient Context Window Extension of Large Language Models · Peng et al., arXiv · published 2023-08 · retrieved 2026-10-02
• Language Model Tokenizers Introduce Unfairness Between Languages · Petrov et al., arXiv · published 2023-05 · retrieved 2026-10-02
• Efficiently Scaling Transformer Inference · Pope et al., arXiv · published 2022-11 · retrieved 2026-10-02
• Transaction Isolation · PostgreSQL documentation · retrieved 2026-10-02
• Table Expressions · PostgreSQL Global Development Group · retrieved 2026-10-05
• Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation · Press et al., arXiv · published 2021-08 · retrieved 2026-10-02
• collections: OrderedDict objects · Python Software Foundation · retrieved 2026-10-05
• heapq: Heap queue algorithm · Python Software Foundation · retrieved 2026-10-05
• Learning Transferable Visual Models From Natural Language Supervision · Radford et al., arXiv · published 2021-02 · retrieved 2026-10-02
• Robust Speech Recognition via Large-Scale Weak Supervision · Radford et al., arXiv · published 2022-12 · retrieved 2026-09-26
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model · Rafailov et al., arXiv · published 2023-05 · retrieved 2026-09-26
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models · Rajbhandari et al., arXiv · published 2019-10-04 · retrieved 2026-09-29. Technical reading on training-state memory and partitioning. The publication date is the first submission; the linked record also lists later revisions. Not interview evidence.
• Algorithms, fourth edition: Directed Graphs · Robert Sedgewick and Kevin Wayne, Princeton University · retrieved 2026-10-05
• Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications · Rodden, Hutchinson and Fu, Google Research · published 2010 · retrieved 2026-09-26
• Are Emergent Abilities of Large Language Models a Mirage? · Schaeffer et al., arXiv · published 2023-04 · retrieved 2026-10-02
• Seamless: Multilingual Expressive and Streaming Speech Translation · Seamless Communication et al., arXiv · published 2023-12-08 · retrieved 2026-10-05
• Neural Machine Translation of Rare Words with Subword Units · Sennrich et al., arXiv · published 2015-08 · retrieved 2026-10-02
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models · Shao et al., arXiv · published 2024-02 · retrieved 2026-09-26
• How to match supply and demand in your marketplace · Sharetribe · retrieved 2026-10-05. Original practitioner account of manual marketplace matching. Vendor-published learning material, not a candidate interview report. Publication date not established.
• GLU Variants Improve Transformer · Shazeer, arXiv · published 2020-02 · retrieved 2026-10-02
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism · Shoeybi et al., arXiv · published 2019-09 · retrieved 2026-10-02
• Prompt injection (series) · Simon Willison · retrieved 2026-09-26
• The lethal trifecta for AI agents: private data, untrusted content, and external communication · Simon Willison · published 2025-06-16 · retrieved 2026-09-26
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters · Snell et al., arXiv · published 2024-08-06 · retrieved 2026-09-29
• RNN Approaches to Text Normalization: A Challenge · Sproat et al., arXiv · published 2016-10 · retrieved 2026-10-02
• Standard Webhooks specification · Standard Webhooks · retrieved 2026-10-02
• Designing robust and predictable APIs with idempotency · Stripe · published 2017 · retrieved 2026-09-26
• Scaling your API with rate limiters · Stripe · published 2017 · retrieved 2026-09-26
• Agentic Commerce Protocol · Stripe Documentation · retrieved 2026-09-26
• RoFormer: Enhanced Transformer with Rotary Position Embedding · Su et al., arXiv · published 2021-04 · retrieved 2026-09-26
• KeyCite · Thomson Reuters · retrieved 2026-10-05. Public vendor explanation of citation treatment; no paid search was accessed. It is not legal advice or a claim of complete jurisdiction coverage.
• Universal Commerce Protocol (UCP) · UCP project · retrieved 2026-09-26
• International AI Safety Report · UK Department for Science, Innovation and Technology · retrieved 2026-10-02
• Attention Is All You Need · Vaswani et al., arXiv · published 2017-06 · retrieved 2026-09-26
• Prevalence and prevention of large language model use in crowd work · Veniamin Veselovsky et al., arXiv · published 2023-10-24 · retrieved 2026-10-05
• vLLM documentation · vLLM project · retrieved 2026-09-26
• HTML Standard: Server-sent events · WHATWG · retrieved 2026-10-05
• Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models · World Health Organization · retrieved 2026-10-05. Health-governance reading, not a clinical validation protocol or deployment approval.
• Small-scale proxies for large-scale Transformer training instabilities · Wortsman et al., arXiv · published 2023-09 · retrieved 2026-10-02
• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models · Xiao et al., arXiv · published 2022-11 · retrieved 2026-10-02
• On Layer Normalization in the Transformer Architecture · Xiong et al., arXiv · published 2020-02 · retrieved 2026-10-02
• ReAct: Synergizing Reasoning and Acting in Language Models · Yao et al., arXiv · published 2022-10 · retrieved 2026-09-26
• Root Mean Square Layer Normalization · Zhang et al., arXiv · published 2019-10 · retrieved 2026-10-02
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena · Zheng et al., arXiv · published 2023-06 · retrieved 2026-09-26
• DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving · Zhong et al., arXiv · published 2024-01 · retrieved 2026-10-02

Job postings (role scope only)

• Forward Deployed Engineer · Anthropic · retrieved 2026-09-29
• Performance Engineer, Inference Systems · Anthropic · retrieved 2026-09-29
• Product Engineer, Computer Use · Anthropic · retrieved 2026-09-29
• Product Manager, New Markets and Monetization · Anthropic · retrieved 2026-09-29
• Research Engineer, Post-Training Model Evaluations · Anthropic · retrieved 2026-09-29
• Research Engineer, Pretraining · Anthropic · retrieved 2026-09-29
• Forward Deployed Engineer (FDE) - Seattle · OpenAI · retrieved 2026-09-29
• Software Engineer, Codex Core Agents · OpenAI · retrieved 2026-09-29