Home Machine Learning Engineer Resume template
Machine Learning Engineer resume template
A clean starting structure with example content grounded in what machine learning engineers actually do day to day, not generic filler. Download and replace the bracketed placeholders with your own details.
Which resume format should you use?
Reverse chronological
You have a steady work history. This is the format almost every recruiter and ATS expects by default.
Functional
You're changing fields or have gaps in your employment. Leads with skills rather than a job-by-job timeline.
Combination
You're early career or have worked consistently but for only a few employers. Blends a skills summary with a shorter chronological history.
This template uses the reverse chronological format: the one most machine learning engineers should default to, since most Australian recruiters and ATS software expect it.
Professional summary
Machine learning engineer with hands-on experience building, training and deploying models that solve concrete business problems. Comfortable moving between data exploration, model development and production monitoring, with a strong grounding in Python and cloud-based ML infrastructure. Works closely with data scientists and stakeholders to turn a vague problem statement into a measurable, working system.
Key skills
- Machine learning model development
- Statistical modelling and evaluation
- Data preprocessing and exploratory analysis
- Cloud infrastructure (AWS SageMaker)
- Python programming
- TensorFlow and Scikit-learn
- Software testing and QA for ML pipelines
- Model monitoring and retraining
Experience: example bullet points
- Built and trained classification and regression models using TensorFlow and Scikit-learn, taking projects from prototype notebook to production-ready code
- Preprocessed and explored large, messy datasets to identify quality issues, reducing downstream errors before model training began
- Deployed trained models to AWS SageMaker and set up monitoring to catch model drift early, cutting the lag between performance drop and retraining
- Worked with data scientists and business stakeholders to translate a loose problem statement into a defined success metric and evaluation plan
- Diagnosed a recurring drift issue in a production model and rebuilt the retraining pipeline so it triggered automatically on new data
- Wrote unit and integration tests for ML pipeline code, catching data schema issues before they reached production
Education
Typically a bachelor degree in computer science, software engineering, mathematics or statistics; many roles now expect or prefer a postgraduate qualification (graduate diploma or masters) with a machine learning or data science focus, plus a portfolio of applied projects.
Keywords an ATS is likely to scan for
Applicant tracking systems match your resume against terms in the job ad before a person ever sees it. Only include the ones that actually apply to your experience, but if a term below matches something you've done, use the same wording the job ad uses.
- Machine learning engineer
- Python
- TensorFlow
- Scikit-learn
- AWS SageMaker
- Jupyter Notebook
- Model deployment
- Model drift
- MLOps
- Statistical modelling
- Data preprocessing
- Cloud infrastructure
- Software testing and QA
- Predictive modelling
Getting past ATS screening
- Match the specific skills, certifications and terms used in the job ad, not just your own wording for the same thing.
- Keep formatting simple: no tables, text boxes, columns, headers/footers or graphics. Parsers frequently drop content placed in these.
- Submit as .docx or PDF unless the job ad specifies otherwise.
- Use standard section headings (Experience, Education, Skills) rather than creative alternatives.
- List your core skills and technical competencies in their own section so a keyword scan can find them instantly.
This is a starting point, not a guarantee of interviews. Tailor every bullet point to your own real experience and the specific job ad.