Quick answer: Machine learning engineer jobs are steady, not booming, in late 2026. One tracker counted 2,436 AI and ML engineer openings at the 853 companies it follows on November 13, 2026, with the count down 1.8% in a month at the same companies while other engineering jobs fell 2.8% (Hiring Tempo). Posted pay is high (roughly $180,000 to $200,000 at the median, depending on the tracker), but the bar is too: the typical data and ML role asks for about 5 years of experience and only about 11% are remote (Ashney, Nov 13, 2026). The fastest way in is to prove production work (deployed models, pipelines, monitoring), search adjacent titles, and apply early.
This guide is for software engineers and data scientists moving into ML engineering, ML engineers who were laid off or want a better role, and OPT or H-1B candidates deciding whether this title is worth targeting. If you lean toward pipelines and platforms, compare it with our data engineer jobs guide. If you are still job hunting on a data science resume, start with the data scientist resume guide and then come back.
Are machine learning engineer jobs in demand right now?
Yes, but the picture depends on who is counting and which titles they include. Treat each row below as a separate snapshot, not one combined total.
| Measure | Figure | Date | Source |
|---|---|---|---|
| AI and ML engineer openings at tracked company careers pages | 2,436 at 853 companies (1,538 in the US) | Nov 13, 2026 | Hiring Tempo |
| Month-over-month change, same 446 companies | 1,398 to 1,373 (down 1.8%); other engineering jobs down 2.8% | Oct 13 to Nov 13, 2026 | Hiring Tempo |
| Open US data and ML engineer roles (combined category) | 8,769 at 2,842 companies; new postings up 14% over 30 days | Nov 13, 2026 | Ashney |
| New US ML engineering postings per week | About 490 on average (13,776 postings analyzed) | Updated Nov 10, 2026 | Axial Search |
| Open machine learning engineer listings on one job site | 2,350 at 313 employers (2,000 most recent analyzed) | Nov 15, 2026 | Roliva |
| Live machine learning engineer listings on a job board | 1,729 | Nov 15, 2026 | Teal |
| Government and defense contractor postings | 713 at 204 employers | Nov 14, 2026 | Ayla |
Three things stand out.
- Demand is steady, not a rush. Hiring Tempo calls it "strong and steady demand, but I would not call it a boom." Ashney found new data and ML postings rose in the past month, but the busiest recent week was the week of October 26, and the latest week was 11% below it.
- Search results look bigger than the real title. Job boards that match loosely show far more results: Jobright returned 6,649 results and Simplify 5,533 for "machine learning engineer" on November 15, 2026, because they include adjacent titles such as software engineer (ML), applied scientist and AI engineer (Jobright, Simplify). That is useful for discovery, but do not read it as the number of true ML engineer roles.
- Many ML jobs are not called that. Hiring Tempo notes that plain "Software Engineer" and "Research Scientist" titles at AI companies are often AI jobs a title search cannot see. Search "applied AI engineer," "ML platform engineer," "MLOps engineer," "applied scientist," "software engineer, machine learning" and "AI engineer" as well.
The wider market is slow but not frozen. US payrolls rose 165,000 in October 2026 and unemployment was 4.1% (BLS Employment Situation, released November 6, 2026). Our October 2026 jobs report breakdown explains what that means for active searchers.
Who is hiring machine learning engineers?
On November 13, 2026, the companies with the most AI and ML engineer openings in Hiring Tempo's count were:
| Company | AI/ML engineer openings | Note |
|---|---|---|
| NVIDIA | 160 | Most by a wide margin |
| ServiceNow | 83 | Up from 57 a month earlier |
| SpaceX | 70 | |
| OpenAI | 68 | Mostly "Applied AI Engineer" titles |
| Hewlett Packard Enterprise | 56 | |
| Waymo | 52 | About one in seven of its open jobs |
| Oracle | 44 | |
| GE Vernova | 42 | |
| Databricks | 41 | |
| Intel | 36 |
Source: Hiring Tempo, Nov 13, 2026. Its sample is mostly tech and large employers with public careers pages, so banks, insurers, retailers and healthcare companies are underrepresented. Ashney lists Amazon as the largest employer by open data and ML roles, with Meta and Apple posting more new roles this month and Capital One fewer (Ashney). In government and defense work, Lockheed Martin (57) and Booz Allen Hamilton (51) led Ayla's count, with Virginia, California and Maryland holding 44% of those postings (Ayla). Many of those roles require a security clearance, which usually means US citizenship.
Where are the jobs?
Location is still concentrated. In Hiring Tempo's US count, San Francisco had 409 openings, New York City 187, Santa Clara 169, Mountain View 96 and Seattle 87. Axial Search found California held 35% of ML engineering postings, followed by Washington and New York at 11% each (Axial Search). If you are not near a hub, government contractors in Virginia and Maryland and large non-tech employers are the main exceptions.
What do machine learning engineer jobs pay?
ML engineering is one of the best-paid engineering titles, but published figures come from postings that disclose pay, which skews toward California, New York and large employers. They are advertised base ranges, not total compensation.
| Measure | Figure | Source |
|---|---|---|
| Median posted range, AI/ML engineer ads with yearly USD pay | $200,000 (low end) to $287,500 (high end), from 291 ads | Hiring Tempo, Nov 13, 2026 |
| Same measure, other engineering ads | $180,000 to $263,000, from 2,572 ads | Hiring Tempo |
| Median ML engineering salary | $197,000 | Axial Search, updated Nov 10, 2026 |
| Typical posted range, US data and ML roles | $141,000 to $201,000 (56% of open roles post pay) | Ashney, Nov 13, 2026 |
| Government and contractor postings, median advertised pay | $178,525 (middle half $151,475 to $207,525) | Ayla, Nov 14, 2026 |
Two caveats. Only about 12% of the AI engineer ads Hiring Tempo tracks list pay at all, and three companies (OpenAI, Zoox and Snowflake) account for 80 of them, so the top number is pulled up by a small group. And the jobs lean senior: about 62% of AI engineer titles include senior, staff, principal or lead, versus 55% of other engineering titles.
The Bureau of Labor Statistics does not track ML engineers as their own occupation. For context only, it reports a May 2025 median wage of $140,300 for computer and information research scientists, with 22% projected growth from 2025 to 2035 (BLS Occupational Outlook Handbook), and $135,980 for software developers, with 10% projected growth (BLS). That supports long-run demand for the skill set, not any one title.
Is your resume reading like a data science or backend resume? Paste it into the free ATS resume score checker with a real ML engineer posting to see which deployment, MLOps and framework keywords are missing, then try find jobs from your resume to see which current openings already fit.
What skills do machine learning engineer job postings ask for?
Every tracker agrees on Python first. After that, the list splits between model work (PyTorch, deep learning, LLMs) and production work (cloud, Kubernetes, MLOps).
| Skill | Roliva (2,000 MLE listings, Nov 15) | Axial Search (ML engineering postings) | Ashney (data and ML roles) |
|---|---|---|---|
| Python | 58% (down from 66% in October) | 80% | 69% |
| Machine learning or deep learning | 47% (machine learning) | 56% (deep learning) | 65% (AI and ML) |
| LLMs | 34% | ||
| Cloud (AWS, Azure, GCP) | AWS 30%, Azure 31%, GCP 14% | 42% (cloud platforms) | AWS 22% |
| PyTorch | 20% | 19% | |
| Kubernetes | 17% | ||
| SQL | 14% | 41% | |
| Docker | 13% | ||
| TensorFlow | 11% |
Sources: Roliva, Axial Search, Ashney. The trackers count differently, so compare the order of skills, not the exact percentages.
What this means in practice: a resume that lists "TensorFlow, Keras, scikit-learn" and a Kaggle notebook reads like 2019. Postings now want proof you can ship and run models, including LLM features, on cloud infrastructure.
How to show each skill on your resume
| What postings want | Weak bullet | Stronger bullet |
|---|---|---|
| Model deployment | "Built ML models" | "Deployed a fraud model behind a FastAPI service on Kubernetes, serving live checkout traffic" |
| MLOps | "Familiar with MLOps" | "Set up training, evaluation and deployment in CI with MLflow, so retrains ship without manual steps" |
| LLM features | "Worked with LLMs" | "Built a retrieval pipeline over support docs with an evaluation set, and tracked answer accuracy before each release" |
| Monitoring | "Monitored models" | "Added drift and latency alerts; caught a feature pipeline break before it affected weekly reporting" |
| Cost and latency | "Optimized performance" | "Cut inference cost by moving batch scoring to spot instances and quantizing the model" |
Use your own real numbers. If you do not have a number, describe the before and after in plain words instead of inventing one.
Experience requirements
The typical data and ML role asks for at least 5 years of experience, and 18% ask for 10 years or more (Ashney). Axial Search found 70% of ML engineering roles are mid-level or senior individual contributor jobs (Axial Search). Employers usually count software engineering or data science years, not only years with the ML engineer title, so a backend engineer with 4 years and real model deployment work can apply to roles asking for 5.
Can you get an entry-level machine learning engineer job?
"Machine learning engineer jobs entry level" is one of the top Google suggestions for this title, and the honest answer is: it is possible, but it is the hardest way in. Intern, new grad and junior titles made up about 5% of AI engineer ads in Hiring Tempo's count, about 120 jobs. On Reddit, one interviewer described 200 applicants for a junior ML position that ended up going to someone with about nine years of experience (r/learnmachinelearning, April 2026). That is one anecdote, but it matches the experience data above.
The routes that work most often:
- Software engineer first, then ML. Join as a backend or platform engineer on a team that ships ML features, take on inference, data pipelines and evaluation, then move internally. Practitioners in the same thread recommend this path.
- Data scientist to ML engineer. Take one of your models all the way to production: packaging, an API, tests, monitoring. That single project changes how your resume reads.
- New grad programs. Large companies still post university ML roles (for example, Adobe listed a 2027 University Graduate Machine Learning Engineer role on Jobright on November 15, 2026). Apply in the first days they open.
- Adjacent titles. Data engineer, MLOps engineer, analytics engineer and "software engineer, AI" roles often have lower experience bars. See our cloud engineer jobs guide if the infrastructure side appeals to you.
A portfolio project that looks like real ML engineering
- Pick a problem with live data (prices, sensor feeds, public APIs) rather than a static Kaggle dataset.
- Train a simple baseline first, then one better model, and write down the tradeoff.
- Serve it behind an API in a container, deploy it to a free or low-cost cloud tier.
- Add an evaluation set, a monitoring check for drift or latency, and a retraining script.
- Write a short README: architecture diagram, what broke, and what you would change at 100 times the traffic.
One working, deployed project you can screen-share beats three notebooks.