Machine Learning Engineer learning roadmap for San Francisco

If you are applying for Machine Learning Engineer roles in San Francisco, the hard part is not finding one listing. The hard part is making each application look specific enough to pass the recruiter screen and the ATS scan.

82 live listings right now.

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Career guide · roadmap

Live Machine Learning Engineer jobs in San Francisco

82 open listings match Machine Learning Engineer in San Francisco right now. The newest:

Skills employers ask for

Terms that keep coming up in the newest Machine Learning Engineer listings in San Francisco, and how many listings use each.
  1. creative21 posts
  2. everyone20 posts
  3. bring20 posts
  4. ideas20 posts
  5. millions22 posts
  6. life20 posts
  7. platform35 posts
  8. staff18 posts
  9. models13 posts
  10. reliable10 posts
  11. hard5 posts
  12. powers8 posts
  13. engineering14 posts
  14. million5 posts
  15. agents8 posts
  16. revenue8 posts
  17. emerging4 posts
  18. rapidly4 posts
  19. research10 posts
  20. software14 posts

Who's hiring

Employers with the most open Machine Learning Engineer listings in San Francisco.
  1. Pinterest18 open jobs
  2. DoorDash USA12 open jobs
  3. Bedrock Robotics5 open jobs
  4. Openai5 open jobs
  5. Anthropic3 open jobs
  6. IBM3 open jobs
  7. LYFT3 open jobs
  8. Together AI3 open jobs
  9. Adobe2 open jobs
  10. Airbnb2 open jobs

Advertised pay

From listings that state pay. Many don't, so treat it as a range, not a promise.

$150,000 to $555,000 a year, median $323,000.

What recruiters will check first in San Francisco

Recruiters scan for role fit, recent proof, and keywords. For Machine Learning Engineer, your resume should show the tools you used, the problem you solved, and the result. A vague project line does not help. A specific line with numbers, users, revenue, cost, speed, quality, or risk reduction works much better.

Check this before sending another application.

  • ATS fit
  • Market proof
  • Interview prep
  • Put the exact job title and adjacent titles in your summary.
  • Mirror the top skills from the job description naturally.
  • Use 3 to 5 achievement bullets that show measurable outcomes.
  • Keep formatting simple so ATS software can parse every section.

Resume keywords for Machine Learning Engineer

Do not stuff keywords. Put them where they belong: skills, project bullets, work experience, and certifications. For Machine Learning Engineer jobs in San Francisco, start with these clusters.

Core skills

role-specific tools, workflows, and delivery methods

Business impact

cost, revenue, speed, quality, risk, retention, or conversion

Collaboration

stakeholders, product, engineering, sales, security, or operations

Proof

portfolio links, shipped work, dashboards, case studies, or GitHub

A practical application plan

Group repeated skills, rewrite your resume for the market, and apply only after the job match is clear.
  1. Pick 10 active Machine Learning Engineer listings in San Francisco and save the job descriptions.
  2. Group repeated skills and phrases. Those are your ATS keywords.
  3. Rewrite your resume summary for this exact market and role.
  4. Run the resume and JD through the JobRise ATS checker.
  5. Only apply after your resume clearly matches the must-have requirements.

More Machine Learning Engineer guides for San Francisco

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Before you apply

Paste the job description into JobRise, check the ATS score, then fix the missing keywords before you send the application. One focused application beats ten generic ones.
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