The ML Engineer List
Live indexThe best machine learning engineer jobs, ranked.
Machine learning engineering pay now runs on three tracks: the AI labs post $250k–$500k base for research engineers (and up to $850k on specialist reqs), Netflix posts $466k–$750k in cash, and big tech posts mid-level bands between roughly $140k and $280k before equity. This is the living index of the ML orgs worth your application — posted pay, which companies post AI engineer and applied-AI tracks, and where residencies and new-grad paths exist, re-verified as the market moves. Every number traces to a posted range, a live careers page, or levels.fyi.
Sources: posted salary ranges, levels.fyi, and company careers pages. Last verified October 11, 2026. How we rank →
OpenAI research engineers build and scale the training and post-training systems behind frontier models, mostly inside Research teams on a dual research engineer / research scientist track, while a separate Applied AI Engineering org builds products and deployments on top. Hiring is rolling through Ashby with one evergreen generic req plus team reqs, and the loop stresses coding and a technical deep dive.
Salary band, hiring status & score breakdown — see the full OpenAI ML Engineer page →
Anthropic research engineers sit inside research teams — pretraining, post-training, RL, interpretability, alignment — in a blended research and engineering role that values code quality and pairing. Hiring is rolling through Greenhouse and mostly in office, and the only early-career route is the paid Fellows program.
Salary band, hiring status & score breakdown — see the full Anthropic ML Engineer page →Anthropic interview guide →
Google ML engineers are hired as software engineers with an AI/ML focus, building ranking, generative AI and infrastructure systems in Cloud AI, Search, Ads and YouTube, while DeepMind research engineers work on Gemini and agents. Hiring is rolling through a structured six-to-eight-week process with a hiring committee.
Salary band, hiring status & score breakdown — see the full Google ML Engineer page →Google interview guide →
Apple ML engineers ship models into Siri, Search, Health, Camera and Apple Intelligence, from on-device runtimes to cloud foundation-model services. Hiring is rolling and req by req with level and pay posted, plus an annual AIML Residency for advanced-degree graduates.
Salary band, hiring status & score breakdown — see the full Apple ML Engineer page →Apple interview guide →
Meta ML engineers build ranking, recommendation, search and integrity models and the systems that train and serve them, plus a Superintelligence Labs track for frontier-model work. Hiring is rolling and level-banded, with one title posted at several pay bands, and the loop runs up to six rounds with an AI assistant in the coding interviews.
Salary band, hiring status & score breakdown — see the full Meta ML Engineer page →Meta interview guide →
NVIDIA ML and deep learning engineers build and optimize models and software for LLM safety, autonomous driving, chip design and GPU performance. Hiring is rolling per req with phone screens and a required onsite round, and the new-grad and internship pipeline is one of the deepest on this list.
Salary band, hiring status & score breakdown — see the full NVIDIA ML Engineer page →
Amazon ML engineers ship models for ads, Prime Video, Music, AWS and the AGI teams, while Applied Scientists own model research and experiments on a separate ladder. Hiring is rolling and posted per team, and the loop weighs Leadership Principles as heavily as technical depth.
Salary band, hiring status & score breakdown — see the full Amazon ML Engineer page →Amazon interview guide →
Netflix ML engineers build ads, personalization and AI-platform systems as senior individual contributors with high autonomy. Hiring is rolling and per team, pay is all cash, and the only early-career ML route is a single PhD internship.
Salary band, hiring status & score breakdown — see the full Netflix ML Engineer page →Netflix interview guide →
Waymo ML engineers build the perception, prediction and planning, simulation and foundation-model (VLM, LLM and world model) systems behind the Waymo Driver. Hiring is req by req through Greenhouse with rolling applications, and early-career talent mostly enters through summer internships.
Salary band, hiring status & score breakdown — see the full Waymo ML Engineer page →Waymo interview guide →
Pinterest ML engineers build the recommendation, ads ranking, search, shopping and visual models behind 640M+ users. Hiring is rolling per req through a recruiter, hiring-manager and team-interview sequence, plus a fall-opening University Grad and ML internship program.
Salary band, hiring status & score breakdown — see the full Pinterest ML Engineer page →Pinterest interview guide →
Microsoft ML engineers and applied scientists build models and agent systems for Copilot, Security, Ads, Edge and CoreAI, usually hired as Applied Scientist or Software Engineer II rather than under an ML Engineer title. Hiring is rolling per req, with US pay bands posted in each description.
Salary band, hiring status & score breakdown — see the full Microsoft ML Engineer page →Microsoft interview guide →
Reddit ML engineers build ads ranking and retrieval, feed and search relevance, content understanding and GenAI and agentic platforms across Consumer and Ads. Hiring is rolling and remote in the US through Greenhouse, and the open ML reqs today start at Senior (IC4).
Salary band, hiring status & score breakdown — see the full Reddit ML Engineer page →Reddit interview guide →
The comp landscape
Posted base-salary bands for each program’s core ML or research engineering req, US market — equity comes on top, and at the AI labs and big tech it can double the number. Bands span levels; senior hires land near the top. Full breakdown in the ML Engineer salary guide.
don’t post salary ranges and are omitted here.
What does a machine learning engineer do?
A machine learning engineer builds and ships the models a product runs on: training and evaluating them, designing the data and feature pipelines that feed them, serving them at scale, and owning the metrics that say whether they work. At the AI labs the same work is called research engineering — building the training, inference and evaluation systems behind frontier models — and a newer applied-AI or AI engineer track builds products on top of those models. Unlike a data scientist, the job is production software engineering; unlike a research scientist, it is measured in shipped systems rather than papers.
The interview loop is consistent across the list: a coding round (often with an ML flavor), an ML system design round, an ML depth round on fundamentals and your past projects, and behavioral. The labs add a research-engineering pairing or take-home, and big tech adds a standard algorithms round. Residency and new-grad paths exist but are small and seasonal, so most hiring is at two or more years of experience.
Preparing for the loop? Start with our guides to the Anthropic machine learning engineer interview, Netflix machine learning engineer interview, and Pinterest machine learning engineer interview.
How we rank
Every program gets an Index score out of 100, built from four weighted dimensions. Scores are editorial judgments grounded in the data below — not paid placements.
Posted ML engineer salary bands, levels.fyi data, and verified offer reports — total comp where an ML-specific sample exists, base bands otherwise.
How real the ML org is: a dedicated ML or research engineering ladder, named teams (pretraining, post-training, inference, applied), the compute behind them, and whether there is a residency or new-grad path.
What the role does for the rest of your career — brand strength, the models and scale you get to work on, and how close the work sits to the frontier.
Open req counts, new applied-AI and AI engineer tracks, residency and entry-level paths, and whether the org is expanding or restructuring.
We also track xAI, Tesla, Databricks, Scale AI, Uber, Stripe, Snap, TikTok and Cohere, currently outside the ranking and queued for the next data pass. OpenAI and Anthropic are ranked on their Research Engineer reqs, Amazon on its Machine Learning Engineer II req alongside the Applied Scientist ladder, and Google and Microsoft on the software and applied-science titles that carry their ML work, because none of the four posts a plain ‘Machine Learning Engineer’ ladder. Total compensation is shown only where levels.fyi has an ML-specific mid-level sample, which is why OpenAI, Anthropic, Microsoft, Waymo and Reddit show base bands alone.
Compensation data comes from posted salary ranges (required in several US states), levels.fyi, and public offer reports; figures are US-market and vary by level and location. Hiring status reflects each company’s careers page as of October 11, 2026. Spotted something outdated? Email [email protected].
Browse ML engineer jobs
Salary & hiring by company
Interview guides
More job lists
Frequently asked questions
Land the offer, not just the interview.
Practice machine learning interview questions, mock interview with peers, and learn the frameworks hiring managers actually score you on.
