Machine Learning Engineer Resume Keywords: What 24 S&P 500 Companies Actually Ask For

What the machine learning engineer data shows

5 terms show up in at least 40% of the 50 machine learning engineer job descriptions we read: Machine learning (72%), Python (64%), AI / LLMs (64%), PyTorch (58%) and Deep learning (42%). Those are the closest thing this role has to a standard keyword set, so a machine learning engineer resume that lacks them is easy for a recruiter's ATS search to skip.

What sets machine learning engineer postings apart from the average S&P 500 posting: Deep learning appears 35x as often, and PyTorch 34.1x and Computer vision 33.3x. These are the role-specific terms generic resume advice tends to miss.

Some terms travel together. Of the 18 machine learning engineer postings that mention TensorFlow, 18 (100%) also mention PyTorch, against 58% across the role. If you list one, check whether you can honestly back up the other.

70.1% of the 97 machine learning engineer postings we indexed carry a senior, staff, lead, principal or manager title.

What machine learning engineer postings actually require

Based on the 50 postings with a full description.

• Experience: median 4.5 years where stated (12 postings specified a number).
• Degrees: 32% list a degree as a requirement and 72% mention one at all.
• Work mode: 28% mention remote, 44% hybrid, 26% on-site language. Mirror the posting's location language in your resume header if you match it.
• Salary: 0% of postings disclosed a range (0 of 50) — too few to report a median.

Job titles in this group include “AI Engineer, Community Support Engineering”, “AI Research Engineer - Datadog AI Research (DAIR)”, “AI Scientist I”, “Senior ML/AI Modeler, Risk Automation Machine Learning”, “Applied Research Scientist”, “Forward Deployed AI Engineer”.

How to use these machine learning engineer keywords without keyword-stuffing

• Use the exact wording for the core terms. "Machine learning", "Python", "AI" and "PyTorch" each appear in 40% or more of machine learning engineer postings, and ATS search is literal, so a near-synonym won't match. Put each one in your skills section and in at least one experience bullet.
• Use the specialist terms as differentiators. TensorFlow (36%), C++ (36%) and LLM fine-tuning / RAG (30%) are less common but far more frequent here than in postings overall. When a posting asks for one and you have it, name it outright. These are the terms that separate a machine learning engineer resume from a generic one.
• State your years of experience. Among the 12 machine learning engineer postings that named a number, the median ask was 4.5 years. Write it in your summary ("4+ years in…") instead of making the recruiter count dates.
• Put your degree where it can be found. 72% of the 50 full machine learning engineer descriptions mention a degree (32% list one as a requirement), so a clearly labeled Education section is worth the space.

Recruiters search their ATS for literal strings, then read the bullets around the match, so each keyword needs to live inside an achievement. For example (illustrative — use your own numbers):

"Fine-tuned a PyTorch deep learning ranking model and shipped it to production, lifting recommendation click-through 9%"

Placement rules are in our keyword placement guide, and the full tailoring workflow is in how to tailor your resume to a job description.