The short answer: MLOps engineers build the platforms other engineers use to train, ship and run models: training infrastructure, feature stores, model serving, and the tooling for rollout and monitoring. At large companies the work is usually posted as a software engineer role on an ML platform or AI infrastructure team, not under an MLOps title. Interviews look like software engineer loops, with coding and system design, plus depth in the platform area. Uber publishes an ML & AI Engineering guide with a loop for each level, and LinkedIn publishes four general hiring stages. Netflix publishes no loop, so its rounds come from a Netflix director's backend article and candidate guides. Last reviewed October 2026.
MLOps Engineer interview: quick answers
| Question | Short answer |
|---|---|
| What does an MLOps engineer do? | Builds and runs shared ML infrastructure: distributed training, feature computation and serving, model serving and LLM inference, and tools for publishing, ramping and monitoring models. Netflix, Uber and LinkedIn all run a central platform for this. |
| What job title should I search for? | Software engineer roles with AI Platform, ML platform, ML Infra or AI Infrastructure in the title. Netflix posts Software Engineer 4/5 to L6/L7 on AI Platform teams, Uber posts Software Engineer roles on Michelangelo, and LinkedIn posts Software Engineer - AI Platform. |
| What do the interviews test? | Coding and system design, as in software engineer loops. Uber lists Software Engineering, Design & Architecture and Collaboration & Leadership. Candidate reports at Netflix and LinkedIn add design of ML platform pieces and a deep dive on past work. |
| Can I use AI tools? | Uber and LinkedIn say no unless the recruiting team tells you in advance. Netflix does not publish a rule; a July 2026 news report says it allows AI while coding, but that is a press report, so ask your recruiter. |
| How much do MLOps engineers make? | Uber lists $202K to $258K base and LinkedIn $120K to $326K base. Netflix lists $466K to $1.69M, but that is a total pay range because Netflix pays salary only with a stock option choice. |
| How is it different from an ML engineer? | At all three, ML engineers (AI Engineers at LinkedIn) build models for one product and use the platform. MLOps and platform engineers build the shared systems those engineers use, with more weight on distributed systems and GPUs. |
What MLOps engineers do
Netflix runs a central AI Platform whose charter is reliable AI tooling and infrastructure for recommendations, studio algorithms, virtual production and other uses. Its open teams in October 2026 were Training Platform, Model Development and Management, Model Serving Systems, Data and Feature Infrastructure and Agent Platform, and the serving platform is being rebuilt to put in-house models, hosted models and external LLM providers behind one layer. Metaflow, the open-source ML workflow library, started at Netflix. Every full-time posting we read was Remote, United States.
Uber's platform is Michelangelo, in-house since 2016. Uber reported about 400 active ML projects, more than 20,000 training jobs a month and 10 million real-time predictions per second at peak in May 2024. Platform postings cover the Michelangelo feature store, the training stack and ML infrastructure for autonomous vehicles, and Uber moved all ML jobs to Ray on Kubernetes in 2024. The roles are filed as Software Engineering jobs with on-call and latency SLOs.
LinkedIn's Software Engineer - AI Platform posting spans four teams: Model Training Infrastructure, the Feature Platform, Model Serving Infrastructure and an ML Ops team for metadata, observability, ramping and experimentation. Postings describe training models with hundreds of billions of parameters on thousands of GPUs, and the team contributes to open-source projects such as Ray, vLLM and DeepSpeed.
The nearest role at each company is the ML engineer. Netflix's Machine Learning Engineers in areas such as Ads build domain models, Uber's ML engineers on teams such as Maps list Michelangelo as a tool they use, and LinkedIn's AI Engineers own product recommender systems end to end. In all three, the platform engineer builds the systems those engineers ship on.
How 3 companies run the MLOps engineer interview
The short version of each process. Each company guide has the full detail, with links to the company's own pages, and says which parts come from candidate reports.
| Company | Screens | Main interviews | AI tools | What stands out |
|---|---|---|---|---|
| Netflix | Recruiter, hiring manager, technical screen (candidate reports) | Four or five 45-minute coding and design interviews (candidate reports) | No published rule; ask recruiter | Remote US; total pay range, not base |
| Uber | 30-minute recruiter call, 60-minute phone screen | 3 to 6 interviews of about 60 minutes, by level | Not allowed unless recruiter says otherwise | Official loop per level; confirm which guide |
| Recruiter call, optional manager call, coding screen (candidate reports) | Several interviews; coding, design, project deep dive (candidate reports) | Not in live interviews unless allowed in advance | Four teams under one AI Platform posting |
The interview types in depth
Coding
Because these jobs are posted as software engineer roles, expect standard coding rounds. Uber runs a 60-minute algorithms and data structures round at every level, with example problems on arrays, strings, two pointers, dynamic programming and search, plus a second coding round in your specialization. Candidate reports for LinkedIn software engineers describe medium problems on arrays, strings, stacks and queues, and Netflix guides describe practical problems tied to the team's work.
Practice medium problems in the language you use daily, and write code that is easy to test. Add platform-flavored tasks: a scheduler, a cache, a rate limiter, a point-in-time join. Say your plan before you code and test edge cases out loud.
ML platform system design
This is where the role differs most from a general backend loop. You design a piece of ML infrastructure: a training platform, a feature store, a model serving layer, a GPU job scheduler or a model ramping system. Uber's Design & Architecture round looks for simple designs, clear trade-offs and reuse of existing systems. Candidate reports at Netflix mention designing an ML job scheduler or an ML platform portal, and serving features by freshness.
A structure that works: name the platform's users and what they do today, then the core path (submit, compute, store, serve), then scale with real numbers for QPS, latency, GPU count and cost. Finish with failure modes, monitoring and how you would roll changes out without breaking users' models.
Depth in your platform area
Interviewers go deep on the area the team owns. Postings show what that means: distributed training with FSDP and tensor or pipeline parallelism, GPU profiling, LLM serving latency and cost, online and offline feature consistency, or streaming feature computation with Kafka and Flink. Uber's second coding round is in your specialization, though it does not publish what that means for ML platform roles.
Read the posting closely and pick its two or three core systems. For each, be able to explain how it works inside, what breaks at scale and how you would measure it. Bring one number from your own work for each.
Deep dive on a past system
Senior loops often spend a round on something you already built. Uber has a Design & Architecture round on a previously solved problem from L5A up, and candidate reports at LinkedIn describe a technical communication round on a past project. LinkedIn also says to be ready to discuss anything you shared in more detail.
Pick one platform you built and know it cold: who used it, its scale, the main trade-offs, an incident, and what you would change. Keep your own decisions separate from the team's.
Behavioral, culture and collaboration
Platform engineers serve other engineers, so behavioral rounds probe how you work with users and partner teams. Uber runs a Collaboration & Leadership round and assesses its values and success attributes. Netflix guides describe leadership and culture interviews from level 5 up and expect you to know the culture memo. Candidate reports at LinkedIn describe a hiring manager behavioral round.
Prepare stories about a platform you built for others, a change that broke a user's model, a call between two teams' requests, and an incident you handled. State your own actions and a measured result.
Pay in US job postings
US salary ranges from each company's own postings for this role, seen October 29, 2026. They are base salary unless the company guide says the posting lists total salary. Each company guide has the postings and links.
| Company | Titles in the postings | Posted US pay range |
|---|---|---|
| Netflix | Software Engineer 4/5, 5 or 6 on an AI Platform team (Training Platform, Model Serving Systems, Data and Feature Infrastructure, Model Development and Management, Agent Platform), and Staff ML Software Engineer (L6) in AIMS Platform Systems. It has no MLOps Engineer title | $466K to $1.69M |
| Uber | Sr Software Engineer - ML platform, Senior Software Engineer - ML Infra and Staff Software Engineer - ML Infra, with body titles such as Senior Software Engineer - AI Platform (Michelangelo Feature Store) and Sr Software Engineer - AI Infra - Training. It has no MLOps Engineer title | $202K to $258K |
| Software Engineer - AI Platform, Staff Software Engineer - AI Platform and Sr. Staff Software Engineer, AI Infrastructure. The AI Platform posting includes an ML Ops (MLOps and Experimentation) sub-team, but LinkedIn has no MLOps Engineer title | $120K to $326K |
Entry-level paths
- Netflix: A paid Machine Learning/AI Infrastructure Engineering Intern (AI Platform) role for PhD students, at least 12 weeks, starting in January 2027.
- Uber: A general 2027 Software Engineering Intern role in San Francisco, Seattle and Sunnyvale that does not name ML platform teams.
- LinkedIn: Technical internships and entry-level hiring, plus a Software Engineer - AI Platform posting that asks for only 1+ years of deep learning systems experience.
AI tools in MLOps engineer interviews
Rules on AI differ by company, so ask your recruiter before every round.
Netflix. Netflix does not publish a rule on AI use in interviews; ask your recruiter. A July 2026 news report says Netflix now lets candidates use AI tools while coding in technical interviews and still assesses code quality, testing and system design. That is a press report that cites no Netflix page.
Uber. Uber's ML & AI Engineering interview guide says: "Unless your recruiter specifically tells you otherwise, AI tools aren't allowed during interviews. This includes tools like ChatGPT, automated response generators, or code auto-completion programs." It adds: "In some technical interviews, you may be provided with an AI agent as part of the coding environment. If that's the case, your recruiter will let you know ahead of time."
LinkedIn. LinkedIn's AI usage page says: "Please do not use AI during live interviews unless the recruiting team has explicitly shared in advance that AI is part of the interview format for that role." For assessments: "Please only use AI tools for coding or specialized assessments if your interviewer or interview module explicitly indicates the use of AI is permitted." It encourages AI for researching roles, exploring concepts and mock interview practice.
A 6-week prep plan
| When | Focus |
|---|---|
| Week 1 | Read your target company's guide and the posting for your team. Note the platform area (training, features, serving or MLOps), the published or reported rounds and the AI rule. Ask your recruiter which rounds you will get. Start the Model Production course. |
| Week 2 | Coding: medium problems under time in your main language, plus platform tasks such as a scheduler, an LRU cache and a point-in-time join, with tests. Use the Data Structures and Algorithms course if you need it. |
| Week 3 | System design basics at scale: caching, queues, storage, consistency and capacity math. Use the System Design course. |
| Week 4 | ML platform design: two problems a week, such as a feature store and a model serving layer, from users to monitoring and rollout. Use the ML System Design and Data Pipelines courses. |
| Week 5 | Depth in your team's area: distributed training and parallelism with the PyTorch course, or GPU inference with the GPU Programming course. Prepare a deep dive on a platform you built. |
| Week 6 | Behavioral and mock loops: write stories for the company's values or culture, then run two full practice loops with follow-up questions and no AI help. |
Courses for each round
Practice questions
Questions from our bank, then practice prompts from the company guides.
Questions from our bank
From the DataInterview question bank, picked for MLOps engineer interviews. Open one to answer it.
Improved Personalization Algorithm
Collaborative Filtering Approach
Cold Start Recommendation
ETA Prediction Model
Ride Demand Forecast
Time Series Demand Forecasting
Candidate Job Matching
Job Recommendation System
Spam Detection System
ML platform design practice
One prompt per company, from the company guides. These are our prompts, not reported interview questions.
Design a training platform for Netflix teams that run distributed PyTorch jobs on Kubernetes and Ray.
Sample Answer
Start with the users: experts who tune parallelism and non-experts who want one call. Cover job submission, GPU scheduling and quotas, data loading, checkpointing to object storage and automatic restart on failure. Add profiling hooks and per-job dashboards so users can see GPU use. Close with how you would roll out SDK changes without breaking running jobs.
Design a real-time feature serving layer for Michelangelo that returns features in milliseconds at high concurrency.
Design a feature platform that lets AI engineers create, compute, store, serve and monitor features online, offline and nearline, at millions of queries per second.
Serving and operations practice
One prompt per company, from the company guides. These are our prompts, not reported interview questions.
After a feature pipeline change, a ranking model's online scores shift but offline metrics look fine. How do you debug it?
Sample Answer
Suspect online and offline skew first. Compare logged online feature values with recomputed offline values for the same requests, feature by feature. Check freshness, default values for missing keys and any change in units or encoding. Roll back the pipeline change if the skew is confirmed, then add a consistency check that would have caught it.
Coding practice
One prompt per company, from the company guides. These are our prompts, not reported interview questions.
Given feature updates (entity, timestamp, value) and label events (entity, timestamp), return the latest feature value at or before each label.
Sample Answer
Group updates by entity and sort by time, then binary search each label's timestamp with bisect_right and take the value just before. Return None when no update exists yet. This point-in-time join avoids leaking future values into training data. It runs in O((n + m) log n).
Given job durations and k GPUs, assign jobs to keep the finish time of the busiest GPU low.
Return the k most frequent feature names in a stream of feature access logs.
ML operations practice
One prompt per company, from the company guides. These are our prompts, not reported interview questions.
Uber's ETA model gets ground truth only when a trip ends. How would you monitor its live accuracy?
Sample Answer
Log every prediction with an ID, then join it with the actual arrival time when the trip ends, which is how Michelangelo tracks live accuracy. Compute error by city and hour on a rolling window and alert on shifts. Watch input feature distributions too, since they move before the labels arrive.
Online and offline values of a feature disagree for 3% of requests. How do you find the cause?
Behavioral practice
Prompts we wrote for MLOps engineer behavioral rounds, with what a strong answer covers.
Tell me about a platform or tool you built for other engineers.
Sample Answer
Say who the users were, what they struggled with before, what you built and how adoption and their speed changed. Include one thing users asked for that you chose not to build, and why.
Tell me about a time a change to your platform broke someone else's model or pipeline.
Tell me about a hard trade-off between cost and reliability.
Tell me about a time two teams wanted different things from your platform.
Are you ready for MLOps engineer interviews?
1 / 5Can you design a training and serving platform for many teams, with real numbers for cost and latency?
Company guides
Read the guide for each company you are interviewing with:
- Netflix: Netflix posts its MLOps work as AI Platform software engineer roles, remote in the US. It publishes no loop, so the rounds here come from a backend article and candidate guides.
- Uber: Uber posts ML platform and ML infra software engineer roles around Michelangelo. Its ML & AI Engineering guide lists the loop by level and a strict AI rule.
- LinkedIn: LinkedIn posts MLOps work as Software Engineer - AI Platform and AI Infrastructure roles. It publishes four hiring stages and exact AI rules; loop details are candidate reports.
- MLOps questions: our practice questions on deployment, serving and monitoring.
Frequently Asked Questions
What is an MLOps engineer interview like?
At large companies it is usually a software engineer loop for an ML platform or AI infrastructure team: coding rounds, system design, and depth in the platform area, plus behavioral rounds. Uber publishes its loop by level, LinkedIn publishes only general stages, and Netflix publishes none.
Why are there so few MLOps Engineer job titles?
Netflix, Uber and LinkedIn all post this work as software engineer roles. Look for AI Platform, ML platform, ML Infra or AI Infrastructure in the title, and read the body, since Uber sometimes puts the specific title there.
Can I use AI tools in MLOps engineer interviews?
Uber says AI tools are not allowed unless your recruiter says otherwise. LinkedIn says not to use AI in live interviews or assessments unless it was explicitly allowed in advance. Netflix does not publish a rule; a July 2026 news report says it allows AI while coding, but ask your recruiter.
How much do MLOps engineers make at Netflix, Uber and LinkedIn?
In US postings seen in October 2026, Uber lists $202K to $224K base for Senior and $232K to $258K for Staff, and LinkedIn lists $120K to $326K base from Software Engineer to Sr. Staff. Netflix lists $466K to $1.69M, which is a total pay range, not base, because Netflix pays salary only.
Do MLOps engineers need to know machine learning?
Yes, at the level of the systems you build. Postings ask for full ML lifecycle knowledge, distributed training, GPU optimization and deep learning frameworks such as PyTorch. They focus less on choosing models and more on training, serving and monitoring them at scale.
What is the difference between an MLOps engineer and an ML engineer?
ML engineers build and ship models for one product, such as Ads at Netflix, Maps at Uber or recommendations at LinkedIn. MLOps and platform engineers build the shared training, feature, serving and monitoring systems those engineers use.
Is the job remote?
It depends on the company. Netflix's AI Platform postings are Remote, United States. Uber asks for at least 50% of time in the office unless approved for full remote work. LinkedIn's postings are hybrid, with office days set by team needs.
