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This index serves as the central knowledge hub for my AI Career Coaching. It aggregates expert analysis on the 2025 AI Engineering market, Transformer architectures, and Upskilling for long-term career growth.
Unlike generic advice, these articles leverage my unique background in Neuroscience and AI to offer a holistic view of the industry. Whether you are an aspiring researcher or a seasoned manager, use the categorized links below to master both the technical and strategic demands of the modern AI ecosystem. 1. Emerging AI Roles (2025)
2. Technical AI Interview Mastery
3. Strategic Career Planning
4. AI Career Advice
Ready to Accelerate Your AI Career? Don't navigate this transition alone. If you are looking for personalized 1-1 coaching to land a high-impact role in the US or global markets: Book a Discovery call
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Book a call to discuss 1-1 coaching and prep for AI Research Engineer roles Table of Contents 1: Understanding the Role & Interview Philosophy
Introduction
The recruitment landscape for AI Research Engineers has undergone a seismic transformation through 2025. The role has emerged as the linchpin of the AI ecosystem, and landing a research engineer role at elite AI companies like OpenAI, Anthropic, or DeepMind has become one of the most competitive endeavors in tech, with acceptance rates below 1% at companies like DeepMind. Unlike the software engineering boom of the 2010s, which was defined by standardized algorithmic puzzles (the "LeetCode" era), the current AI hiring cycle is defined by a demand for "Full-Stack AI Research & Engineering Capability." The modern AI Research Engineer must possess the theoretical intuition of a physicist, the systems engineering capability of a site reliability engineer, and the ethical foresight of a safety researcher. In this comprehensive guide, I synthesize insights from several verified interview experiences, including from my coaching clients, to help you navigate these challenging interviews and secure your dream role at frontier AI labs. 1: Understanding the Role & Interview Philosophy 1.1 The Convergence of Scientist and Engineer Historically, the division of labor in AI labs was binary: Research Scientists (typically PhDs) formulated novel architectures and mathematical proofs, while Research Engineers (typically MS/BS holders) translated these specifications into efficient code. This distinct separation has collapsed in the era of large-scale research and engineering efforts underlying the development of modern Large Language Models. The sheer scale of modern models means that "engineering" decisions, such as how to partition a model across 4,000 GPUs, are inextricably linked to "scientific" outcomes like convergence stability and hyperparameter dynamics. At Google DeepMind, for instance, scientists are expected to write production-quality JAX code, and engineers are expected to read arXiv papers and propose architectural modifications. 1.2 What Top AI Companies Look For Research engineer positions at frontier AI labs demand:
1.3 Cultural Phenotypes: The "Big Three" The interview process is a reflection of the company's internal culture, with distinct "personalities" for each of the major labs that directly influence their assessment strategies. OpenAI: The Pragmatic Scalers OpenAI's culture is intensely practical, product-focused, and obsessed with scale. The organization values "high potential" generalists who can ramp up quickly in new domains over hyper-specialized academics. Their interview process prioritizes raw coding speed, practical debugging, and the ability to refactor messy "research code" into production-grade software. The recurring theme is "Engineering Efficiency" - translating ideas into working code in minutes, not days. Anthropic: The Safety-First Architects Anthropic represents a counter-culture to the aggressive accelerationism of OpenAI. Founded by former OpenAI employees concerned about safety, Anthropic's interview process is heavily weighted towards "Alignment" and "Constitutional AI." A candidate who is technically brilliant but dismissive of safety concerns is a "Type I Error" for Anthropic - a hire they must avoid at all costs. Their process involves rigorous reference checks, often conducted during the interview cycle. Google DeepMind: The Academic Rigorists DeepMind retains its heritage as a research laboratory first and a product company second. They maintain an interview loop that feels like a PhD defense mixed with a rigorous engineering exam, explicitly testing broad academic knowledge - Linear Algebra, Calculus, and Probability Theory - through oral "Quiz" rounds. They value "Research Taste": the ability to intuit which research directions are promising and which are dead ends. 2: The Interview Process 2.1 OpenAI Interview Process Candidates typically go through four to six hours of final interviews with four to six people over one to two days. Timeline: The entire process can take 6-8 weeks, but if you put pressure on them throughout you can speed things up, especially if you mention other offers Critical Process Notes: The hiring process at OpenAI is decentralized, with a lot of variation in interview steps and styles depending on the role and team - you might apply to one role but have them suggest others as you move through the process. AI use in OpenAI interviews is strictly prohibited Stage-by-Stage Breakdown: 1. Recruiter Screen (30 min)
2. Technical Phone Screen (60 min)
3. Possible Second Technical Screen
4. Virtual Onsite (4-6 hours) a) Presentation (45 min)
b) Coding (60 min)
c) System Design (60 min)
d) ML Coding/Debugging (45-60 min)
e) Research Discussion (60 min)
f) Behavioral Interviews (2 x 30-45 min sessions)
OpenAI-Specific Technical Topics: Niche topics specific to OpenAI include time-based data structures, versioned data stores, coroutines in your chosen language (multithreading, concurrency), and object-oriented programming concepts (abstract classes, iterator classes, inheritance) Key Insights:
2.2 Anthropic Interview Process The entire process takes about three to four weeks and is described as very well thought out and easy compared to other companies Timeline: Average of 20 days Stage-by-Stage Breakdown: 1. Recruiter Screen
2. Online Assessment (90 min)
3. Virtual Onsite a) Technical Coding (60 min)
b) Research Brainstorm (60 min)
c) Take-Home Project (5 hours)
d) System Design
e) Safety Alignment (45 min)
Key Insights:
2.3 Google DeepMind Interview Process Timeline: Variable, can be lengthy Stage-by-Stage Breakdown: 1. Recruiter Screen
2. The Quiz (45 min)
3. Coding Interviews (2 rounds, 45 min each)
4. ML Implementation (45 min)
5. ML Debugging (45 min)
6. Research Talk (60 min)
Key Insights:
3: Interview Question Categories & Deep Preparation 3.1: Theoretical Foundations - Math & ML Theory Unlike software engineering, where the "theory" is largely limited to Big-O notation, AI engineering requires a grasp of continuous mathematics. The rationale is that debugging a neural network often requires reasoning about the loss landscape, which is a function of geometry and calculus. 3.1.1 Linear Algebra Candidates are expected to have an intuitive and formal grasp of linear algebra. It is not enough to know how to multiply matrices; one must understand what that multiplication represents geometrically. Key Topics:
3.1.2 Calculus and Optimization The "Backpropagation" question is a rite of passage. However, it rarely appears as "Explain backprop." Instead, it manifests as "Derive the gradients for this specific custom layer". Key Topics:
3.1.3 Probability and Statistics Key Topics:
3.2: ML Coding & Implementation from Scratch The Transformer Implementation The Transformer (Vaswani et al., 2017) is the "Hello World" of modern AI interviews. Candidates are routinely asked to implement a Multi-Head Attention (MHA) block or a full Transformer layer. The "Trap" of Shapes: The primary failure mode in this question is tensor shape management. Q usually comes in as (B, S, H, D). To perform the dot product with K (B, S, H, D), one must transpose K to (B, H, D, S) and Q to (B, H, S, D) to get the (B, H, S, S) attention scores. The PyTorch Pitfall: Mixing up view() and reshape(). view() only works on contiguous tensors. After a transpose, the tensor is non-contiguous. Calling view() will throw an error. The candidate must know to call .contiguous() or use .reshape(). This subtle detail is a strong signal of deep PyTorch experience. The Masking Detail: For decoder-only models (like GPT), implementing the causal mask is non-negotiable. Why not fill with 0? Because e^0 = 1. We want the probability to be zero, so the logit must be -∞. Common ML Coding Questions:
3.3: ML Debugging Popularized by DeepMind and adopted by OpenAI, this format presents the candidate with a Jupyter notebook containing a model that "runs but doesn't learn." The code compiles, but the loss is flat or diverging. The candidate acts as a "human debugger". Common "Stupid" Bugs: 1. Broadcasting Silently: The code adds a bias vector of shape (N) to a matrix of shape (B, N). This usually works. But if the bias is (1, N) and the matrix is (N, B), PyTorch might broadcast it in a way that doesn't make geometric sense, effectively adding the bias to the wrong dimension 2. The Softmax Dimension: F.softmax(logits, dim=0). In a batch of data, dim=0 is usually the batch dimension. Applying softmax across the batch means the probabilities sum to 1 across different samples, which is nonsensical. It should be dim=1 (the class dimension) 3. Loss Function Inputs: criterion = nn.CrossEntropyLoss(); loss = criterion(torch.softmax(logits), target). In PyTorch, CrossEntropyLoss combines LogSoftmax and NLLLoss. It expects raw logits. Passing probabilities (output of softmax) into it applies the log-softmax again, leading to incorrect gradients and stalled training 4. Gradient Accumulation: The training loop lacks optimizer.zero_grad(). Gradients accumulate every iteration. The step size effectively grows larger and larger, causing the model to diverge explosively 5. Data Loader Shuffling: DataLoader(dataset, shuffle=False) for the training set. The model sees data in a fixed order (often sorted by label or time). It learns the order rather than the features, or fails to converge because the gradient updates are not stochastic enough Preparation Strategy:
3.4: ML System Design If the coding round tests the ability to build a unit of AI, the System Design round tests the ability to build the factory. With the advent of LLMs, this has become the most demanding round, requiring knowledge that spans hardware, networking, and distributed systems algorithms. Distributed Training Architectures The standard question is: "How would you train a 100B+ parameter model?" A 100B model requires roughly 400GB of memory just for parameters and optimizer states (in mixed precision), which exceeds the 80GB capacity of a single Nvidia A100/H100. The "3D Parallelism" Solution: A passing answer must synthesize three types of parallelism: 1. Data Parallelism (DP): Replicating the model across multiple GPUs and splitting the batch. Key Concept: AllReduce. The gradients must be averaged across all GPUs. This is a communication bottleneck 2. Pipeline Parallelism (PP): Splitting the model vertically (layers 1-10 on GPU A, 11-20 on GPU B). The "Bubble" Problem: The candidate must explain that naive pipelining leaves GPUs idle while waiting for data. The solution is GPipe or 1F1B (One-Forward-One-Backward) scheduling to fill the pipeline with micro-batches 3. Tensor Parallelism (TP): Splitting the model horizontally (splitting the matrix multiplication itself). Hardware Constraint: TP requires massive communication bandwidth because every single layer requires synchronization. Therefore, TP is usually done within a single node (connected by NVLink), while PP and DP are done across nodes The "Straggler" Problem: A sophisticated follow-up question: "You are training on 4,000 GPUs. One GPU is consistently 10% slower (a straggler). What happens?" In synchronous training, the entire cluster waits for the slowest GPU. One straggler degrades the performance of 3,999 other GPUs 3.5 Inference Optimization Key Concepts:
3.6 RAG Systems: For Applied Scientist roles, RAG is a dominant design topic. The Architecture: Vector Database (Pinecone/Milvus) + LLM + Retriever. Solutions include Citation/Grounding, Reranking using a Cross-Encoder, and Hybrid Search combining dense retrieval (embeddings) with sparse retrieval (BM25) Common System Design Questions:
Framework:
3.7: Research Discussion & Paper Analysis Format: Discuss a paper sent a few days in advance covering overall idea, method, findings, advantages and limitations What to Cover:
Discussion of Your Research:
Preparation:
3.8: AI Safety & Ethics In 2025, technical prowess is insufficient if the candidate is deemed a "safety risk." This is particularly true for Anthropic and OpenAI. Interviewers are looking for nuance. A candidate who dismisses safety concerns as "hype" or "scifi" will be rejected immediately. Conversely, a candidate who is paralyzed by fear and refuses to ship anything will also fail. The target is "Responsible Scaling". Key Topics: RLHF (Reinforcement Learning from Human Feedback): Understanding the mechanics of training a Reward Model on human preferences and using PPO to optimize the policy Constitutional AI (Anthropic): The idea of replacing human feedback with AI feedback (RLAIF) guided by a set of principles (a "constitution"). This scales safety oversight better than relying on human labelers Red Teaming: The practice of adversarially attacking the model to find jailbreaks. Candidates might be asked to design a "Red Team" campaign for a new biology-focused model Additional Topics:
Behavioral Red Flags: Social media discussions and hiring manager insights highlight specific "Red Flags": The "Lone Wolf" who insists on working in isolation; Arrogance/Lack of Humility in a field that moves too fast for anyone to know everything; Misaligned Motivation expressing interest only in "getting rich" or "fame" rather than the mission of the lab Preparation:
3.9: Behavioral & Cultural Fit STAR Method: Situation, Task, Action, Result framework for structuring responses Core Question Types: Mission Alignment:
Collaboration:
Leadership & Initiative:
Learning & Growth:
Key Principles:
4: Strategic Career Development & Application Playbook The 90% Rule: It's What You Did Years Ago 90% of making a hiring manager or recruiter interested has happened years ago and doesn't involve any current preparation or application strategy. This means:
The Groundwork Principle: It took decades of choices and hard work to "just know someone" who could provide a referral - perform at your best even when the job seems trivial, treat everyone well because social circles at the top of any field prove surprisingly small, and always leave workplaces on a high note Step 1: Compile Your Target List
Step 2: Cold Outreach Template (That Works) For cold outreach via LinkedIn or Email where available, write something like: "I'm [Name] and really excited about [specific work/project] and strongly considering applying to role [specific role]. Is there anything you can share to help me make the best possible application...". The outreach template can also be optimized further to maximize the likelihood of your message being read and responded. Step 3: Batch Your Applications Proceed in batches with each batch containing one referred top choice plus other companies you'd still consider; schedule lower-stakes interviews before top choice ones to get routine and make first-time mistakes in settings where damage is reasonable Step 4: Aim for Multiple Concurrent Offers Goal is making it to offer stage with multiple companies simultaneously - concrete offers provide signal on which feels better and give leverage in negotiations on team assignment, signing bonus, remote work, etc. The Essence:
Building Career Momentum Through Strategic Projects When organizations hire, they want to bet on winners - either All-Stars or up-and-coming underdogs; it's necessary to demonstrate this particular job is the logical next step on an upward trajectory The Resume That Gets Interviews: Kept to a single one-column page using different typefaces, font sizes, and colors for readability while staying conservative; imagined the hiring manager reading on their phone semi-engaged in discussion with colleagues - they weren't scrolling, everything on page two is lost anyway Four Sections:
Each entry contains small description of tasks, successful outcomes, and technologies used; whenever available, added metrics to add credibility and quantify impact; hyperlinks to GitHub code in blue to highlight what you want readers to see How to Build Your Network: Online (Twitter/X specifically): Post (sometimes daily) updates on learning ML, Rust, Kubernetes, building compilers, or paper writing struggles; serves as public accountability and proof of work when someone stumbles across your profile; write blog posts about projects to create artifacts others may find interesting Offline: o where people with similar interests go - clubs, meetups, fairs, bootcamps, schools, cohort-based programs; latter are particularly effective because attendees are more committed and in a phase of life where they're especially open to new friendships The Formula:
5: Interview-Specific Preparation Strategies Take-Home Assignments Takehomes are programming challenges sent via email with deadline of couple days to week; contents are pretty idiosyncratic to company - examples include: specification with code submission against test suite, small ticket with access to codebase to solve issue (sometimes compensated ~$500 USD), or LLM training code with model producing gibberish where you identify 10 bugs Programming Interview Best Practices They all serve common goal: evaluate how you think, break down problem, think about edge cases, and work toward solution; companies want to see communication and collaboration skills so it's imperative to talk out loud - fine to read exercise and think for minute in silence, but after that verbalize thought process If stuck, explain where and why - sometimes that's enough to figure out solution yourself but also presents possibility for interviewer to nudge in right direction; better to pass with help than not work at all Language Choice: If you could choose language, choose Python - partly because well-versed but also because didn't want to deal with memory issues in algorithmic interview; recommend high-level language you're familiar with - little value wrestling with borrow checker or forgetting to declare variable when you could focus on algorithm Behavioral Interview Preparation The STAR Framework: Prepare behavioral stories in writingusing STAR framework: Situation (where working, team constellation, current goal), Task (specific task and why difficult), Action (what you did to accomplish task and overcome difficulty), Result (final result of efforts) Use STAR when writing stories and map to different company values; also follow STAR when telling story in interview to make sure you do not forget anything in forming coherent narrative Quiz/Fundamentals Interview Knowledge/Quiz/Fundamentals interviews are designed to map and find edges of expertise in relevant subject area; these are harder to specifically prepare for than System Design or LeetCode because less formulaic and are designed to gauge knowledge and experience acquired over career and can't be prepared by cramming night before Strategically refresh what you think may be relevant based on job description by skimming through books or lecture notes and listening to podcasts and YouTube videos. Sample Questions: Examples:
Best Response When Uncertain: Best preparation is knowing stuff on CV and having enough knowledge on everything listed in job description to say couple intelligent sentences; since interviewers want to find edge of knowledge, it is usually fine to say "I don't know"; when not completely sure, preface with "I haven't had practical exposure to distributed training, so my knowledge is theoretical. But you have data, model, and tensor parallelism..." 6: The Mental Game & Long-Term Strategy The Volume Game Reality Getting a job is ultimately a numbers game; you can't guarantee success of any one particular interview, but you can bias towards success by making your own movie as good as it can be through history of strong performance and preparing much more diligently than other interviewees; after that, it's about fortitude to keep persisting through taking many shots at goal Timeline Reality: Competitive jobs at established companies or scale-ups take significant time - around 2-3 months; then takes 2 weeks to negotiate contract and couple more weeks to make switch; so even if everything goes smoothly (and that's an if you cannot count on), full-time job search is at least 4 months of transitional state The Three Principles for Long-Term Success Always follow these principles: 1) Perform at your best even when job seems trivial or unimportant, 2) Treat everyone well because life is mysteriously unpredictable and social circles at top of any field prove surprisingly small, 3) Always leave workplaces on a high note - studies show people tend to remember peaks and ends: what was your top achievement and how did you end? 7: The Complete Preparation Roadmap 12-Week Intensive PreparationWeeks 1-4 (Foundations):
Weeks 5-8 (Implementation):
Weeks 9-10 (Systems):
Weeks 11-12 (Mock & Culture):
8 Conclusion: Your Path to Success The 2025 AI Research Engineer interview is a grueling test of "Full Stack AI" capability. It demands bridging the gap between abstract mathematics and concrete hardware constraints. It is no longer enough to be smart; one must be effective. The Winning Profile:
Remember the 90/10 Rule: 90% of successfully interviewing is all the work you've done in the past and the positive work experiences others remember having with you. But that remaining 10% of intense preparation can make all the difference. The Path Forward: In long run, it's strategy that makes successful career; but in each moment, there is often significant value in tactical work; being prepared makes good impression, and failing to get career-defining opportunities just because LeetCode is annoying is short-sighted Final Wisdom: You can't connect the dots moving forward; you can only connect them looking back—while you may not anticipate the career you'll have nor architect each pivotal event, follow these principles: perform at your best always, treat everyone well, and always leave on a high note 9 Ready to Crack Your AI Research Engineer Interview? Landing a research engineer role at OpenAI, Anthropic, or DeepMind requires more than technical knowledge - it demands strategic career development, intensive preparation, and insider understanding of what each company values. As an AI scientist and career coach with 17+ years of experience spanning Amazon Alexa AI, leading startups, and research institutions like Oxford and UCL, I've successfully coached 100+ candidates into top AI companies. I provide:
Ready to land your dream AI research role? Book a discovery call to discuss your interview preparation strategy.  I. Introduction This recent survey of 8000+ tech professionals (May 2025) by Lenny Rachitsky and Noam Segal caught my eye. For anyone interested in a career in tech or already working in this sector, it is a highly recommended read. The blog is full of granular insights about various aspects of work - burnout, career optimism, working in startups vs. big tech companies, in-office vs. hybrid vs. remote work, impact of AI etc. However, the insight that really caught my eye is the one shared above highlighting the impact of direct-manager effectiveness on employees' sentiment at work. It's a common adage that 'people don't leave companies, they leave bad managers', and the picture captured by Lenny's survey really hits the message home. The delta in work sentiment on various dimensions (from enjoyment to engagement to burnout) between 'great' and 'ineffective' managers is so obviously large that you don't need statistical error bars to highlight the effect size! The quality of leadership has never been more important given the double whammy of massive layoffs of tech roles and the impact of generative AI tools in contributing to improved organisational efficiencies that further lead to reduced headcount. In my recent career coaching sessions with mentees seeking new jobs or those impacted by layoffs, identifying and avoiding toxic companies, work cultures and direct managers is often a critical and burning question. Although one may glean some useful insights from online forums like Blind, Reddit, Glassdoor, these platforms are often not completely reliable and have poor signal-to-noise in terms of actionable advice. In this blog, I dive deeper into this topic and highlight common traits of ineffective leadership and how to identify these traits and spot red flags during the job interview process. II. Common Characteristics of Ineffective Managers These traits are frequently cited by employees:
The interview process is a two-way street. It's your opportunity to assess the manager and the company culture. Here's how to look for red flags, based on advice shared in online communities: A. During the Application and Initial Research Phase:
B. During the Interview(s): How the Interviewer Behaves:
The importance of intuition and trusting your gut cannot be overemphasised enough. If something feels "off" during the interview process, even if you can't pinpoint the exact reason, pay attention to that feeling. The interview is often a curated glimpse into the company; if red flags are apparent even then, the day-to-day reality at work could be much worse. By combining common insights from fellow peers and mentors with careful observation and targeted questions during the interview process, you can significantly improve your chances of identifying and avoiding incompetent, inefficient, or toxic managers and finding a healthier, more supportive work environment. 1-1 Career Coaching for Evaluating Great Managers and Mentors
As this guide demonstrates, your manager is the single most important factor in your job satisfaction, career growth, and daily work experience. Yet most candidates spend more time preparing technical questions than evaluating the person they'll report to. This is a costly mistake - one that leads to burnout, stunted growth, and premature departures. The Manager Impact:
Your Interview Framework:
Common Interview Mistakes:
Why Interview Coaching Makes the Difference: Evaluating managers requires skills many candidates haven't developed:
Optimize Your Manager Evaluation: With 17+ years working under and alongside diverse managers - from exceptional mentors to cautionary tales - I've developed frameworks for assessing manager quality during interviews. I've coached 100+ candidates through offer evaluations where manager assessment changed their decision, often saving them from toxic situations and guiding them toward transformative opportunities. What You Get:
Next Steps:
Contact: Email me directly at [email protected] with:
You'll spend more time with your manager than almost anyone else in your life. Choosing well is one of the highest-ROI career decisions you'll make. Don't leave it to chance - prepare to evaluate managers as rigorously as they evaluate you. Let's ensure your next role sets you up for success, not regret. Cracking data science and, increasingly, AI interviews at top-tier companies has become a multifaceted challenge. Whether you're targeting a dynamic startup or a Big Tech giant, and regardless of the specific level, you should be prepared for a rigorous interview process that can involve 3 to 6 or even more rounds. While the core areas remain foundational, the emphasis and specific expectations have evolved. The essential pillars of data science and AI interviews typically include:
Here's a more detailed breakdown:
Navigating the Evolving Interview LandscapeGiven the increasing complexity and variability of data science and AI interviews, the advice to learn from experienced mentors is more critical than ever. Here's why:
In conclusion, cracking data science and AI interviews in 2025 requires a strong foundation in core technical areas, an understanding of AI system design principles, solid product and business acumen, excellent communication skills, and increasingly, a grasp of fundamental data structures and algorithms. Learning from experienced mentors who have navigated these challenging interviews successfully is an invaluable asset in your preparation journey. 1-1 Career Coaching for Mastering Data Science Interviews
Data Science interviews are uniquely challenging - combining coding, statistics, machine learning, system design, and communication. As this comprehensive guide demonstrates, success requires mastery across multiple domains and strategic preparation tailored to specific company formats and role expectations. The DS Interview Landscape:
Your 80/20 for DS Interview Success:
Common Interview Preparation Mistakes:
Why Structured Interview Prep Matters: DS interviews are complex and company-specific. Generic preparation wastes time and misses critical areas:
Accelerate Your DS Interview Success: With experience spanning academia, industry, and coaching - successfully preparing 100+ candidates for DS roles at Meta, Amazon, LinkedIn, and fast-growing startups - I've developed comprehensive frameworks for DS interview mastery. What You Get:
Next Steps:
Contact: Email me directly at [email protected] with:
Data Science interviews are among the most multifaceted in tech. Success requires balanced preparation across multiple domains and strategic focus on company-specific requirements. With structured coaching, you can prepare efficiently and confidently - maximizing your chances of landing your target role. Let's crack your DS interviews together. |
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