Machine Learning Engineer resume
What the hiring manager reads first
All roles Software engineering Reading notes, not rules
Summary
An engineering manager usually looks first for what the candidate built or worked on, the system around it and the stack it ran on.
Also posted as: ml engineer, ai engineer, applied scientist, deep learning engineer, llm engineer, applied ml engineer.
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Read first
- Models shipped to production and what they changed
- Offline and online metrics you moved
- Data and training infrastructure
- Frameworks and serving stack
One line, before and after
Original: Trained a ranking model in PyTorch for the feed. In an A/B test it raised click through rate by 4.2%.Suggested: Raised feed click through rate by 4.2% in an A/B test with a ranking model trained in PyTorch.
Why this changedThe online result leads; the model and framework follow. Every figure in the rewrite is in the original line; nothing is added.
Section order
- Summary
- Experience
- Selected projects or papers
- Skills
- Education
Where your resume has these sections. A section your resume does not have is not added.
Mistakes that cost interviews
- Benchmark scores with no production result
- Coursework presented as work
- A wall of technologies with no context
- Team achievements written as personal ones
- Seniority words the resume does not support
Evidence that counts in software engineering
- Systems or features worked on, and the candidate's part in them
- Scale: users, requests, data volume, latency, cost
- Measured effect on reliability, performance or cost
- Stack named where it was used, not only in a skills list
- Code review, mentoring and design work for senior roles
- Shipped side projects or open source for early careers
Use what your resume already shows. A line without a number can still be strong when it says clearly what you did and where.
These are common reading conventions drawn from hiring guides and career services, not rules every employer follows.