What Makes the Anthropic System Design Interview Hard? Many questions seem simple at first, but the follow-ups can go much deeper. The interviewer may keep pushing on how you identify the problem, reduce the risk, and prove that your solution actually works. Here's one example: Your team is deploying a conversational AI model that handles sensitive topics. During internal testing, you find that the model tends to give confidently wrong answers in high-risk situations. How would you investigate and address this? Let's break it down step by step. First, identify the problem Start with evaluation. Look at the model's calibration and uncertainty. Compare confidence scores with accuracy on held-out sensitive cases, and analyze where the failures are happening. Then, figure out how to address it Once you understand the failure modes, move on to mitigation. You could consider retrieval augmentation for more reliable context, abstention thresholds for cases where the model is uncertain, or a separate verification layer for high-risk outputs. Finally, prove that it actually worked This may be where the interviewer pushes the hardest: How do you know it's actually fixed? Accuracy alone isn't enough. You also need to consider calibration, abstention quality, performance across different risk levels, and ongoing monitoring to make sure the model isn't simply developing new failure modes. That's what makes this question harder than it initially looks. It's not about one specific technical solution. You need to take a seemingly basic AI problem and keep breaking it down: identify the problem, understand the cause, reduce the risk, and use the right evaluation to prove the changes actually worked. Click the link to see the full question and the candidate's answer: screna.ai/experience/50113d5…

From SDE to GenAI, 5 ML Interview Prep Tips You Should Know 1. ML System Design: Build a Framework + Mock Often Start with common questions and real interview experiences. Build a framework for each type of problem, from business goals to model, data, training, deployment, evaluation, and monitoring. For candidates with limited ML interview experience, mock interviews with more experienced people can be especially valuable. Doing multiple rounds with different people helps uncover gaps and exposes different ways to approach the same problem. 2. ML Coding: Go From "Understanding" to "Writing" Prepare for model implementation, common ML modules, classification/regression, core building blocks, and training/inference logic based on real use cases. The goal is not just to understand the concepts, but to write and explain them from scratch. Repeating the same problems until the implementation feels natural can make a big difference. 3. LeetCode: Core Patterns + Recent Interview Questions For candidates with an SDE background, start with a structured set like NeetCode 150, then focus on recent interview questions from target companies. Pay particular attention to problems that have appeared repeatedly in the past few months, and review them multiple times before the interview. 4. ML Fundamentals: Build Your Own Knowledge System ML fundamentals can take the most time to prepare. Beyond classic papers, technical blogs, and community resources, organize the knowledge into your own system. For each topic, think about the intuition, visualization, theory, examples, and common interview questions. Reviewing these areas repeatedly makes it easier to handle follow-ups beyond simple definitions. 5. Project Deep Dive: Start With Your Resume Project Deep Dive preparation can start with your resume. Getting feedback forces you to think through why you made certain decisions, what trade-offs you considered, what problems you encountered, and how you measured the results. These are also the areas interviewers are most likely to explore during a deep dive.

What should engineers bet on in the GenAI era? 1. Applied GenAI may offer the biggest opportunity GenAI IC roles today roughly fall into four areas: Research, LLM Training, LLM Inference, and Applied GenAI. In terms of compensation and entry barriers, the order is still roughly Research > Training > Inference > Applied. But the job market may look very different: Applied GenAI > Inference > Training > Research. Research and training roles are limited by nature. Only a relatively small number of companies are building foundation models. Meanwhile, almost every company has its own data, workflows, and use cases for GenAI. That makes Applied GenAI a much larger market. It can also provide a path into AI infrastructure and inference work. 2. The core of Applied GenAI is moving from RAG toward Context Engineering + Agents RAG became the standard approach over the past two years. Now the conversation is shifting toward Context Engineering and Agents. In real production systems, though, Context Engineering is far more complicated than a simple RAG demo. You have to deal with everything from ingestion, embeddings, indexing, and storage to search and ranking, while keeping latency low, availability high, and recall strong. Agents make things even harder with repeated retrieval and more complex context management. The real competition may not be about who can call a model API. It will be about who can build and operate a complex AI system reliably at scale. 3. AI will reduce the need for pure coding, but engineering skills will matter more Coding tools like Claude can already boost productivity significantly. But complex requirements still require careful problem definition, precise prompts, code review, and iteration. The difficult part is gradually shifting from “How do I write this code?” to “How should this system be designed?”, “What is the right solution?”, and “How do we debug and operate it in production?” That makes system design, AI infrastructure, complex investigations, and understanding customer needs increasingly valuable. 4. Engineers also need to understand business As AI makes software development cheaper, the more important question may become: What is actually worth building? Lower development costs could create more software demand and make small teams or even one-person companies much more viable. Engineers therefore cannot focus only on technology. Domain knowledge and business sense will become increasingly important. The valuable engineer of the future may not just be someone who can build things, but someone who understands what to build and why it matters. 5. The job is changing, not simply disappearing The number of pure coding roles will likely decline, while opportunities in Applied GenAI, AI infrastructure, system design, and AI combined with domain expertise may grow. The transition will not be smooth. AI-generated code still has bugs, production systems still have uncertainty, and someone still needs to take responsibility when things go wrong. So the goal should not be to compete with AI at writing code. It should be to learn how to use AI to solve harder problems, design better systems, and create real business value. I’m still optimistic about the future. We are clearly going through a painful transition, but every major technology shift changes which skills are valuable and creates new opportunities along the way.

The OpenAI system design question that looks easy until question #4: Design a text-to-video platform similar to Sora. Everyone opens the same way. API, job queue, GPU workers, storage. Clean diagram, confident five minutes. Then it gets ugly: 1️⃣ A GPU worker gets marked dead, job retries elsewhere. Original worker reconnects, tries to publish anyway. Congrats, you might've just double-charged someone. 2️⃣ Job is 80% done when the spot instance gets yanked. Restart from zero, or did you actually design for this? 3️⃣ Inference succeeds. Watermarking fails after. Retry everything? Retry nothing? 4️⃣ One prompt crashes every worker that touches it. Nobody catches this until it's already happening. 5️⃣ GPU utilization is 60% and the queue keeps growing. Everyone assumes "add more GPUs." Do you actually know that? 6️⃣ Client times out, retries. Did you just spin up a second job and bill twice? None of these are trick questions. This is just what happens when a system meets a world that doesn't cooperate. Full breakdown of how one candidate handled OAI SD, where cracked, what the interviewer pushed hardest on, on Screna AI screna.ai/experience/afa6ba5…

Most people prepare for interviews by memorizing STAR stories. But if the STAR answer is solid, why do you still fail to get a strong signal? The problem may not be the story. It may be how you tell it. Here are some of the most common interview communication mistakes:

Quick guide to SDE System Design interview questions 1. Classic Questions The old questions aren’t necessarily hard, but forgetting the key algorithms when you actually get asked can be painful. At least go through URL Shortener, Rate Limiter, and Typeahead. The goal isn’t to memorize a standard answer. Understand the core design and the trade-offs. 2. The New Trend: AI Agent Design These questions can roughly be split into two types. Chatbot-style systems: Text chatbot, Voice customer support using Temporal + LLM + Voice-to-Text, and Video chatbot using RAG + multimodal models. Then there’s RAG + AI Agent: for example, document reading + rule-based systems built with a two-pipeline architecture. These questions are becoming more common because many AI companies are building products around exactly these workflows, such as analyzing logistics and procurement documents, CPA tax forms, and healthcare documents. 3. High-Frequency: Payment Async Events This is one area worth preparing seriously. For a single-process design, you may need to handle events from third-party APIs or webhooks. Key concepts include event status control, idempotency keys, and pessimistic vs. optimistic locking. For fan-out, the payment event needs to be consumed by multiple downstream services. The key question is how to reliably fan out the event after it has already been persisted in the DB. Be comfortable with Kafka, partitions, consumer groups, outbox, inbox, and relay patterns. 4. Locks / Concurrency Common mechanisms include distributed Redis locks (SET key val NX EX), pessimistic locks (SELECT ... FOR UPDATE SKIP LOCKED), optimistic locks (UPDATE ... WHERE version = ?), and unique constraints. Don’t just memorize the syntax. Know when to use a Redis lock vs. pessimistic or optimistic locking, and when SKIP LOCKED actually makes sense. 5. AI Agent-Specific Concepts AI System Design interviews can easily go deeper here: How do you prevent hallucinations? Think grounding, verifiers, and structured output. How do you reduce token usage? Think context compression, prompt caching, and structured state. How do you improve quality? Think user feedback, offline eval + CI, and realtime verifiers. You should also be ready to discuss input/output guardrails. And have numbers. For example, know your average ReAct loop count and token cost. Instead of saying “it depends,” give a baseline such as 2 rounds and $0.08 total, then explain when and why those numbers would change.

4 Steps to Build a Strong, Defensible Resume with AI Give AI your full background Upload your old resume. Then tell AI about the projects, work, and side projects you’ve done recently. Upload relevant docs, notes, and other materials for each project. Think of this as the baseline. AI needs to know what you’ve actually done before it can ask better questions or help you turn those experiences into resume bullets. Grill yourself on every project Use /grill-me or /grill-with-docs and go through every project in your career. Not every project needs to be resume-worthy. Let AI dig into the details: What did you actually do? What was your contribution? What was the impact? How big was the scope? Why did you make those decisions? What problems did you run into? A lot of the best resume material is buried in these details. Turn everything into a career-archive.md Once you’re done with the grilling, ask AI to turn everything into a career-archive.md. It should include every project you discussed, along with a short description, your contributions, scope, impact, and other useful details. This file can become much more than a resume source. Keep updating it as your career grows. It can eventually become a long-term archive of your professional experience. Build the resume from your career archive Now ask AI to write your resume based on career-archive.md. Then fine-tune the details yourself. Reorder projects. Change what you want to emphasize. Make certain parts more aggressive when needed. After these four steps, the resume is probably 80% done. The last 20% still needs human judgment. A lot of people use AI to write resumes by dropping in their old resume and a job description and asking it to “optimize it.” But AI doesn’t always understand your experience, so the result can miss the key points or oversell things that are hard to defend. Give AI enough context and let it dig into your experience to build a strong, defensible resume.

6 Things LLM Inference Interviews Test Traditional Coding Many companies still test coding, but it’s not always pure LeetCode. Some use practical or scenario-based coding instead. For senior roles, coding is more of a hard requirement: doing well may not be a major differentiator, but failing can prevent you from reaching the onsite stage. So it doesn’t need to be the biggest part of your prep, but it shouldn’t be ignored. Inference System Design This is one of the most important parts of LLM Inference interviews. Common topics include LLM serving, continuous batching, KV cache, prefix caching, prefill/decode, distributed inference, scheduling, and the tradeoffs between latency, throughput, memory, compute, and communication. These interviews are usually open-ended, so instead of memorizing a standard answer, build real technical depth in a few core areas and be ready to handle multiple levels of follow-up questions. ML Coding This is especially common at frontier AI companies. Questions may involve implementing an autoregressive generation loop, attention, parts of a Transformer, forward/backward propagation, or tensor manipulation. Compared with traditional coding, the focus is usually less on complex algorithms and more on whether candidates genuinely understand the underlying mechanics of Transformers and inference rather than simply knowing how to use a framework. Project Deep Dive Candidates are typically asked to walk through a representative project in depth. The key areas are Depth + Impact: Why did the problem matter? What was the bottleneck? Why was this architecture chosen? What alternatives were considered? What was the hardest problem? What did the candidate personally own? And what was the final performance or business impact? For senior/staff roles, this is also an important opportunity to demonstrate technical level. Behavioral Behavioral interviews are relatively standard, usually covering leadership, conflict, failure, ambiguity, cross-team collaboration, technical influence, and impact. As seniority increases, the focus shifts from how impressive the story sounds to what it reveals about the candidate’s scope, ownership, and influence. Traditional Distributed Systems Design Some companies still include traditional distributed systems design, such as designing a storage system, messaging system, or distributed service. However, this is not usually the biggest focus for LLM Inference teams. If prep time is limited, it generally makes sense to prioritize the five areas above first. Takeaway: For senior LLM Inference roles, the key is building genuine technical depth. That depth may need to extend from batching to schedulers, KV cache, distributed communication, GPU/NPU memory hierarchy, kernels, networking, and the full serving stack.

Tech hiring is down. AI hiring isn't. With Senior AI Engineer base salaries around $230K in the U.S., it's worth knowing what skills AI Engineers actually need and what kind of projects can help you stand out. Here's what to focus on :🧵

⏰No time to grind Coding questions before your interview? Try this framework. A lot of Coding interview questions can actually be grouped into one big category: Traversal. For each category, I focus on the core idea, a fixed template, and 2–3 representative problems. First, ask: what information do I need to maintain while traversing? The key isn't just traversing the array. It’s maintaining a data structure while processing the current element, so you can quickly get the information you need. For example, Sliding Window maintains a window, while Next Greater Element maintains a Monotonic Stack. Choose the data structure based on what you need to maintain Common ones include Array, HashMap, Sorted List, Sorted Array, Heap, Monotonic Stack, and Deque. The key isn't memorizing these data structures. It’s recognizing what the problem needs: fast lookup, sorting, comparison, or maintaining some kind of monotonic relationship. Learn the update template Once you’ve picked the data structure, you need to know when to add, remove, or update elements as you traverse. A lot of this comes down to using if and while correctly. For Monotonic Stack problems in particular, the while loop is often the key part of the solution. Practice 2–3 representative problems If you’re short on time, you don’t need to grind dozens of similar questions. Use 2–3 problems to really understand the whole flow: Core Idea → Data Structure → Update Template → Index Handling When you see a Coding question, ask yourself: What am I traversing? What do I need to maintain? What data structure should I use? How should I update it? Did I get the indexes right? This is much more useful than simply memorizing the solution to a specific problem. If you’re preparing for tech interviews, screna.ai/ provides real interview experiences from major tech companies, along with AI mock interviews. Hope it helps!

Hey @X Algorithm 👋 We’re here to share real interview experiences, useful interview tips, and what’s happening in tech hiring!📈 Looking to connect with people who are: 💻 Preparing for tech interviews 🚀 Trying to break into tech 🔎 Actively job hunting 👀 Curious about what companies are really asking 🤝 Interested in the tech industry If that’s you, say hi in the comments! I’ll check out as many as I can.🙌

🚨Want to ace your MLE interview? You can’t ignore these 8 key LLM & NLP areas. LLM fundamentals Tokenization, embeddings, attention, context windows, positional encoding, KV cache, etc. Transformers & model architecture Self-attention, MHA vs. MQA/GQA, encoder vs. decoder, MoE, normalization, residual connections, and why these design choices matter. Training & fine-tuning Pretraining, SFT, RLHF/RLAIF, LoRA/QLoRA, instruction tuning, data quality, and training efficiency. Inference & serving Quantization, batching, KV cache, speculative decoding, latency vs. throughput, GPU memory, and scaling inference. RAG & retrieval Chunking, embeddings, vector search, hybrid search, reranking, context construction, and how to deal with bad retrieval. Evaluation How do you actually know your LLM is getting better? Offline evals, benchmarks, human evaluation, hallucination, relevance, groundedness, and online A/B testing. NLP fundamentals Classification, sequence labeling, NER, sentiment analysis, text similarity, and traditional NLP concepts that still show up in interviews. Production & System Design The questions eventually move beyond “what is X?” and start testing how you’d actually build and scale it in the real world — what happens when traffic increases 10x, where the bottlenecks are, and how you’d balance model quality, latency, and cost. These takeaways are based on 65 LLM & NLP interview questions. If you’re preparing for an MLE interview, Screna AI also has real interview experiences and targeted mock interviews that can help you practice these topics in a more interview-focused way!

Tech interview difficulty, based on candidate feedback Which tier are you challenging? 🔴 Tier 0 | OpenAI / Anthropic OpenAI OA questions are relatively easy to prepare for thanks to the amount of interview data available online, but some are coding heavy, and even getting the solution right doesn’t always guarantee a pass. Anthropic Harder to prepare for because there are fewer interview reports available, with a strong focus on completion and code quality. 🔴 Tier 0.5 | Databricks Databricks Heavy coding + system programming. 4 coding rounds and 1 design round, with algorithm questions reaching Hard and some system programming questions going beyond typical LeetCode. 🟠 Tier 1 | Meta / Airbnb / DoorDash Meta Mostly Medium to easier Hard, but you may need to solve 2 questions in 45 minutes, often with limited IDE debugging. Airbnb / DoorDash Hard level coding questions and generally more challenging coding rounds. 🟡 Tier 2 | Roblox / TikTok / Apple Roblox Team dependent, with some onsites focusing heavily on system design and coding that doesn’t follow the typical LeetCode format. TikTok Coding and system design can be mixed across rounds, with the bar varying by team. Apple Highly team dependent, with some teams using their own interview questions, making the bar harder to predict. Preparing for interviews at any of these companies? screna.ai/ has a rich library of interview experiences from these companies, plus targeted mock interviews to help you prepare more effectively!

Preparing for AI Engineer interviews at top tech companies? 💻Here are the key areas you need to know first:

In an AI-assisted coding interview ❌Just ask AI to analyze the code and find the bugs. ✅ The right steps 01 Understand first Read the requirements, README, expected behavior, and identify the problem yourself. 02 Ask small questions Don't dump the entire problem into AI. Narrow the search space step by step. 03 Build a mental model Understand the code flow, functions, state, and dependencies before claiming you're done. 04 Verify everything Test AI's hypothesis. Check edge cases. Look for hallucinations and regressions. 05 Own the reasoning You should be able to explain what changed, why it works, and why you chose this approach. 06 Know the fundamentals Big-O, data structures, algorithms, memory, and technical trade-offs still matter. 07 Measure and iterate Test, benchmark, compare alternatives, and use the results to guide the next iteration. AI-assisted coding interviews are becoming increasingly common. Screna AI has collected real interview questions and experiences from these interviews, so you can see what to expect and practice before you're put on the spot.

In an AI-assisted coding interview ❌Just ask AI to analyze the code and find the bugs. ✅ The right steps 01 Understand first Read the requirements, README, expected behavior, and identify the problem yourself. 02 Ask small questions Don't dump the entire problem into AI. Narrow the search space step by step. 03 Build a mental model Understand the code flow, functions, state, and dependencies before claiming you're done. 04 Verify everything Test AI's hypothesis. Check edge cases. Look for hallucinations and regressions. 05 Own the reasoning You should be able to explain what changed, why it works, and why you chose this approach. 06 Know the fundamentals Big-O, data structures, algorithms, memory, and technical trade-offs still matter. 07 Measure and iterate Test, benchmark, compare alternatives, and use the results to guide the next iteration. AI-assisted coding interviews are becoming increasingly common. Screna AI has collected real interview questions and experiences from these interviews, so you can see what to expect and practice before you're put on the spot.