# Machine Learning Interview Prep (2026 Guide) Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/blog/machine-learning-interview-guide Source observed: 2026-11-04T22:31:43.645Z Last checked: 2026-11-04T22:31:43.645+00:00 Indexed: 2026-11-06T17:41:14.567+00:00 --- # Machine Learning Interview Prep (2026 Guide) We created this guide for candidates preparing for machine learning engineer interviews in 2026. It explains the interview loop, recent question patterns, company-specific differences, the DRIVE system-design framework, preparation schedules, and key ML concepts. Aced is formerly Exponent. **Updated:** a month ago, as shown on the source page. The guide is based on accounts from candidates who recently interviewed at Meta, Waymo, and Anthropic, and from a Pinterest interviewer who ran more than 50 ML loops. Explore our [interview guides](https://www.aced.io/guides?ref=exponent-blog.ghost.io) and [recent ML interview experiences](https://www.aced.io/experiences?ref=exponent-blog.ghost.io). ## What a machine learning interview covers A machine learning interview is a multi-round loop assessing coding, applied ML, system design, and behavioral skills. It commonly starts with a recruiter screen and a coding screen, followed by a final loop of four to six rounds. The exact mix depends on the company, team, and level. | Round | What it evaluates | Common format | |---|---|---| | Recruiter screen | Background, level fit, years of experience | Conversation, no code | | Coding screen | Data structures and algorithms | Shared editor, one or two questions | | ML coding | Applied ML in code, including numpy, pandas, and simple model preparation | Data manipulation on a small sample | | ML system design | Recommendation, ranking, or retrieval-system design | Whiteboard or shared-document discussion | | ML practitioner / ML depth | Theory connected to your projects | Discussion of concepts and trade-offs | | Behavioral | Leadership, conflict, and cross-team influence | Project walkthrough and situational questions | Our [ML engineer interview course](https://www.aced.io/courses/ml-engineer?ref=exponent-blog.ghost.io) covers these round types with worked examples and mock sessions. ML engineering interviews combine the software-engineering coding bar with applied ML. A candidate may pass an algorithmic screen but struggle with ML coding or ML system design, while a strong ML candidate may fail the algorithmic screen. ## What changed in 2026 The coding bar for ML roles is approaching the software-engineering bar. Interviewers also expect candidates to design and ship ML systems, not only discuss model theory. ### Coding is closer to software-engineering level A recent senior/staff Waymo loop included two full coding rounds and a separate ML coding round after a medium-to-hard pattern-based screen. A Meta L5 onsite included three algorithmic coding rounds covering trees, graphs, and string parsing. ### Recommendation and retrieval dominate system design Recent prompts center on recommendation, ranking, and retrieval in the company's product context. Pinterest commonly asks candidates to design recommendation engines for related pins, search ranking, or the personalized home feed. Waymo has asked about retrieving similar driving scenes for training data, and Meta has asked about recommending locations to a user arriving in a new city. ### Applied ML coding is separate ML coding tests different skills from standard algorithmic coding. A recent Waymo round used numpy on a small 3D trajectory sample, tokenizing data to predict the next step. Prepare for numpy APIs, broadcasting, and means across multidimensional vectors. Ask the recruiter whether “coding” means algorithms, basic Python, numpy manipulation, or framework work such as building PyTorch layers. ### Safety and data ethics appear in AI-lab loops A recent Anthropic entry-level loop combined SQL and Python data extraction with ethical and regulatory trade-offs. Its three-person panel covered model misuse, alignment, and data privacy, using a scenario about investigating and mitigating a conversational model that gives confident but incorrect answers in high-risk contexts. ### Behavioral rounds may include technical walkthroughs Some behavioral rounds require a detailed walkthrough of a past project. In a recent Waymo leadership round, interviewers examined the technical challenge as closely as the candidate's leadership. Start with a concise project overview, then expand on the areas raised by follow-up questions. ## Example questions ### Coding and algorithms - Find every character in string A that also appears in string B. (Meta, L5) - Traverse a grid to find the shortest path between two points. (Meta, L5) - Count mismatched brackets in a string containing three bracket types. (Meta, L5) - Find the lowest common ancestor of two tree nodes, then find it without traversing to the root. (Meta, L5) - Model a company org chart as a tree and return an employee's direct reports and department. (Waymo, senior/staff) - Read a diagram of two moving obstacles and compute how they collide. (Waymo, senior/staff, team-specific) ### ML coding - Tokenize a small 3D trajectory sample with numpy so the current trajectory can predict the next step. (Waymo, senior/staff) - Extract and clean a dataset using only SQL and Python, without external NLP libraries. (Anthropic, entry-level) - Transform a messy 100-column dataset with missing values and duplicates into a clean downstream dataset from an intentionally ambiguous prompt. (Anthropic, entry-level) ### ML system design - Retrieve similar driving scenes for an input to assemble training data. (Waymo, senior/staff) - Design a recommendation feed of locations for a user arriving in a new city. (Meta, L5) - Build a recommendation engine for related pins, search ranking, or the home feed. (Pinterest, reported by an interviewer) ### ML practitioner depth - Explain L1 versus L2 regularization using a technique from a past project. (Pinterest) - Your model's accuracy is low. What do you do first? (Pinterest) When a deep-learning model trains slowly and underperforms, diagnose the bottleneck before changing a hyperparameter. Identify whether the problem is training time, overfitting or underfitting, or a particular data slice. Changing batch size first may require retuning other hyperparameters; immediately adding data or model depth can also address the wrong problem. For worked practice, use our [ML interview question roundup](https://www.aced.io/blog/top-machine-learning-interview-questions?ref=exponent-blog.ghost.io). ## Interviews by company ### Meta [Meta's ML engineering interview](https://www.aced.io/guides/meta-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) is coding-heavy, with system design and behavioral rounds. A recent L5 loop included a recruiter screen, an ungraded three-part file-system assessment, live coding, a recruiter debrief on graded metrics, and an onsite with three coding rounds, one ML system-design round, and one behavioral round. Meta evaluates code presentation and explanation as well as execution. The recruiter debrief considered clarity, whether the interviewer could follow the code, and time and space complexity. Narrate your approach and state complexity while solving. Practice trees, graphs, and string parsing. ### Anthropic [Anthropic's entry-level ML loop](https://www.aced.io/guides/anthropic-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) combines applied data work with safety and ethics. A recent loop included a recruiter screen, a technical assessment with SQL, Python, and ethical or regulatory questions, live coding, a three-person panel, and a final onsite with a Python transformation task and debugging of a roughly 200-line model-training pipeline. Prepare for model misuse, alignment, data privacy, data cleaning, transformation, and pipeline debugging. See our [real interview experiences](https://www.aced.io/experiences?ref=exponent-blog.ghost.io). ### Waymo [Waymo's ML loop](https://www.aced.io/guides/waymo-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) applies a software-engineering coding bar and adds domain-specific questions. A recent senior/staff loop included a recruiter screen, a medium-to-hard pattern-based coding screen, another coding round, ML coding with numpy trajectory data, ML system design for retrieving similar driving scenes, and two behavioral rounds including one focused on leadership. The senior and staff loops are the same, with leveling decided at the end. Coding is Google-style: one harder question rather than two simpler questions. Prioritize algorithms, then numpy manipulation, retrieval-oriented system design, and a leadership-focused project story. ### Pinterest [Pinterest's ML interview](https://www.aced.io/guides/pinterest-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) emphasizes ML system design and ML depth tied to Pinterest products. The loop includes a recruiter screen, tech screen, and onsite with at least two coding rounds, one ML system-design round, one ML-practitioner round, and a behavioral round. Coding often focuses on graphs and trees in Pinterest terms, such as finding similar images across a graph. Common system-design red flags are ignoring the recommendation feedback loop, not understanding calibration, and claiming that nothing could go wrong. Define the label and objective early, explain trade-offs, and complete the core structure quickly enough to explore two areas deeply. Prepare around search, ads ranking, and home-feed recommendations, and connect ML-practitioner answers to your resume projects. ### More company guides - [Google MLE interview](https://www.aced.io/guides/google-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Amazon MLE interview](https://www.aced.io/guides/amazon-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Microsoft MLE interview](https://www.aced.io/guides/microsoft-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Apple MLE interview](https://www.aced.io/guides/apple-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Netflix MLE interview](https://www.aced.io/guides/netflix-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Snap MLE interview](https://www.aced.io/guides/snap-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [TikTok MLE interview](https://www.aced.io/guides/tiktok-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - Reddit MLE interview - [FanDuel MLE interview](https://www.aced.io/guides/fanduel-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) ## The DRIVE framework Use DRIVE to structure ML system-design answers: 1. **Define the problem and success metric.** State the objective, task type—ranking, retrieval, or classification—and label. Explain why the framing is supervised when applicable. 2. **Retrieve the data.** Cover sourcing, sampling from a large store, and featurization. Examples include similar-scene sampling for Waymo and location features for a Meta feed. 3. **Iterate on the model.** Discuss architecture, two-tower models, learning-rate decay, batch size, training, and tuning. Diagnose the bottleneck before adding data or depth. 4. **Validate the results.** Select offline and online metrics, calibration, and slice-based error analysis. 5. **Enter production.** Cover serving, monitoring, retraining, and the recommendation feedback loop. Adapt the depth to the prompt. Anthropic may emphasize misuse and data ethics, while Waymo may emphasize domain-specific data sampling. ## Preparation plans Focus first on pattern-based coding and ML system design. Our [ML engineer interview course](https://www.aced.io/courses/ml-engineer?ref=exponent-blog.ghost.io) and [mock interview practice](https://www.aced.io/practice?ref=exponent-blog.ghost.io) support this preparation. ### Eight-week plan | Weeks | Focus | Details | |---|---|---| | 1 to 2 | Data structures and algorithms | Trees, graphs, sliding window, and string parsing at medium-to-hard difficulty | | 3 to 4 | ML coding | Numpy and pandas fluency, small-sample manipulation, and simple preprocessing | | 5 to 6 | ML system design (DRIVE) | Build two recommendation or retrieval designs end to end; explore two areas deeply in each | | 7 | ML practitioner depth | Regularization, calibration, feedback loops, and deep-learning tuning tied to your projects | | 8 | Behavioral | Technical and leadership project stories plus mock sessions | ### Two-week plan | Days | Focus | Details | |---|---|---| | 1 to 4 | Coding | Medium-to-hard pattern challenges focused on trees and graphs | | 5 to 8 | ML coding and system design | One ML coding pass and two DRIVE-structured design walkthroughs | | 9 to 11 | ML practitioner | Concepts and calibration | | 12 to 14 | Behavioral | Stories and full mock loops | ## Key concepts - **L1 versus L2 regularization:** Weight penalties and when to use each. - **Recommendation feedback loop:** How recommendations influence the later training data. - **Calibration:** Why predicted probabilities should match observed rates, especially in ads ranking. - **Two-tower models:** Separate query and candidate encoders for retrieval at scale. - **Deep-learning tuning:** Batch size, learning-rate decay, and checks for diverging loss. - **Offline versus online evaluation:** Metrics before and after deployment. - **Numpy fluency:** Broadcasting and means over multidimensional vectors. - **BFS and DFS:** Core techniques for tree and graph coding questions. ## FAQs ### How long is the interview loop? Expect a recruiter screen, a coding screen, and usually four to six additional rounds spread across one or more days. The final loop may include one or two more coding rounds, ML coding, ML system design, and one or two behavioral rounds. Ask the recruiter which rounds apply and how they are scheduled. ### How does ML coding differ from standard coding? ML coding usually tests numpy or pandas manipulation and light model preprocessing. Standard coding tests data structures and algorithms such as trees, graphs, and string parsing. Confirm whether a round requires basic Python, numpy, pandas, or a framework such as PyTorch, and prepare for both forms. ### Do ML interviews still ask algorithms? Yes. Recent loops include medium-to-hard trees, graphs, sliding-window, and string-parsing questions, sometimes framed around the company's product. ### What is ML system design? It is an end-to-end design exercise for a machine-learning system, commonly recommendation, ranking, or retrieval. Move through the problem and metric, data and features, model, evaluation, and production. Discuss calibration and the recommendation feedback loop. ### How does an ML engineer interview differ from a software engineer interview? It adds ML coding, ML system design, and ML depth to the software-engineering algorithmic coding bar. Prepare across both software and applied-ML skills. ### How should I prepare with limited time? Start with medium-to-hard pattern-based coding and a reusable system-design structure for recommendation or ranking prompts. Then practice numpy and pandas, review regularization, calibration, and feedback loops, and prepare behavioral stories with technical depth. ## Aced preparation resources - [Browse ML engineer interview questions](https://www.aced.io/questions?role=ml-engineer&src=blog) - [Book time with a Machine Learning Engineer coach](https://www.aced.io/coaching?role=ml-engineer): mock interviews, career coaching, and resume review - [Create a free account](https://www.aced.io/signup?src=blog) - [ML Engineer Interview Prep Course](https://www.aced.io/courses/ml-engineer): 6 courses, 9,400 students - [Behavioral Interviews: A Practical Guide for Engineers](https://www.aced.io/courses/eng-behavioral): 28 lessons The page includes a stated **96% rating (5,400 reviews)** for Aced's preparation offering. It also links to: - [ML interview questions with worked answers](https://www.aced.io/blog/top-machine-learning-interview-questions?ref=exponent-blog.ghost.io) - [Machine learning system design interview guide](https://www.aced.io/blog/machine-learning-system-design-interview-guide?ref=exponent-blog.ghost.io) - [Recent ML interview experiences](https://www.aced.io/experiences?ref=exponent-blog.ghost.io) - [ML question bank](https://www.aced.io/questions?type=machine-learning&ref=exponent-blog.ghost.io) ### Related reading - [How to Become a Machine Learning Engineer](https://www.aced.io/blog/become-a-machine-learning-engineer) - [How to Become a Machine Learning Engineer](https://www.aced.io/blog/become-machine-learning-engineer) - [ML Engineer Resume Guide and Templates](https://www.aced.io/blog/machine-learning-engineer-resume) - [Data Scientist vs. Machine Learning Engineer: Key Differences and Career Insights](https://www.aced.io/blog/data-scientist-vs-machine-learning-engineer) --- [Read this page’s summary](https://www.aced.io/agents/api/public/v1/page-tldr?url=https%3A%2F%2Fwww.aced.io%2Fblog%2Fmachine-learning-interview-guide&trace=t_679ff9ab98f3) ## Ask a follow-up about this page Need a more specific answer? 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