Resume template · Updated October 2026
Machine learning engineer resume template
Short answer
Catalyst fits a senior machine learning engineer: it skips the summary, so the model you run in production, its traffic and its latency are the first lines a reader sees. The fictional sample owns search ranking at an outdoor gear marketplace and, before that, moved a streaming service's recommendations from nightly batch to real-time serving. Bullets cover features, retraining, monitoring and mentoring.
Download the machine learning engineer sample
Isaac is a fictional senior machine learning engineer. Replace every detail with your own before you send it.
The files are made in your browser by Resumity's own exporters, from this sample. The builder opens it as your own resume: click any line to replace it. Free plan, no watermark.
Why Catalyst suits a machine learning engineer resume
Catalyst prints no summary and opens on the latest job, which is where a senior ML engineer's strongest evidence sits: a model serving real requests. Its large left-aligned name and teal headings in IBM Plex Sans give a technical look, and the skills follow the jobs as labelled rows.
Second choice: FAANGPath. FAANGPath keeps a summary and prints the skills block first; pick it if you are moving into ML engineering from data science or backend work.
What goes in each section, in Catalyst's order
Catalyst prints its sections in a fixed order, the same in the PDF and the Word file. Here is that order for this sample, and what to put in each section.
- Work Experience. The headline does the work a summary would: "Senior Machine Learning Engineer, Ranking and Recommendations". Then write the model and its serving numbers (requests per second, p99 latency), the pipeline around it, and one online result from an A/B test, kept separate from offline metrics.
- Skills. Split modelling from infrastructure: PyTorch, LightGBM and learning to rank in Technical; Spark, Kubeflow, MLflow, Kubernetes and the cloud ML service in Tools. ML engineering screens read the Tools row as closely as the frameworks.
- Education. Degree and field on one line. A master's in computer science or a related field is common for ML roles; if your thesis was on a related method, a short note can sit with it.
- Certifications. A cloud ML certification is optional for senior engineers and prints last. If you list AWS Certified Machine Learning Engineer - Associate, give the year: AWS says it is valid for three years.
The sample resume as text
The same content as the files above, so you can copy a line or compare it with your own.
Isaac Thornton
Senior Machine Learning Engineer, Ranking and Recommendations
Salt Lake City, UT | isaac.thornton@example.com | (801) 555-0193 | github.com/sample-isaac-thornton | linkedin.com/in/sample-isaac-thornton
Experience
Senior Machine Learning Engineer, Gearloop Marketplace, Salt Lake City, UT (04/2022 to Present)
- Own the search ranking service: a LightGBM learning-to-rank model behind a Go API at about 1,500 requests a second, with p99 latency under 80 ms.
- Built the Spark feature pipeline and Redis online store so training and serving share one set of feature definitions, removing a skew that had hidden a drop in click-through.
- Set up weekly retraining in Kubeflow Pipelines with an offline evaluation gate and shadow traffic before each rollout.
- Added drift alerts on input features and score distributions; broken upstream data is now caught before ranking quality falls.
- Mentor three ML engineers and run the team's fortnightly model review.
Machine Learning Engineer, Sawtooth Streaming, Boise, ID (06/2018 to 03/2022)
- Moved title recommendations from nightly batch scoring to a real-time TensorFlow Serving endpoint, so new viewing shows up in the next session.
- Cut GPU training cost by about a third with mixed precision and spot instances.
- Wired every model change into the experiment platform so it shipped behind an A/B flag.
Education
MS, Computer Science, Wasatch State University (05/2018)
Certifications
- AWS Certified Machine Learning Engineer - Associate, Amazon Web Services (2025)
Skills
Python, PyTorch, LightGBM, Learning to rank, Feature engineering, Go (model serving), SQL, Apache Spark, Kubeflow Pipelines, MLflow, Redis, Kubernetes, AWS (SageMaker, S3), Prometheus, Model reviews, Mentoring
How to make the sample yours
Give serving numbers, not just model scores
Requests per second, latency at p99, model size and cost per thousand predictions tell an ML platform lead that you have run models in production. Pull them from your dashboards.
Keep offline and online results apart
NDCG on a validation set and click-through in an A/B test are different claims. Write both if you have them, and never present an offline gain as a business result.
Say which MLOps pieces you built and which you used
The sample built the feature pipeline and the retraining gate. If your company had a platform team, write "on the in-house feature store" so nobody expects you to have designed it.
Swap ranking for your problem area
Fraud, forecasting, computer vision and ads each have their own vocabulary (precision at a threshold, MAPE, mAP, calibration). Rebuild the bullets around your models in those terms.
Show one data-quality catch
Training-serving skew, a broken upstream feed or label leakage you found and fixed is a story every ML interviewer asks for. Keep a bullet for it.
Keywords to keep where they are true
Terms this kind of resume is usually searched for. Use the posting's exact wording, and only for work you have done.
- machine learning engineering
- model serving
- MLOps
- PyTorch
- LightGBM
- learning to rank
- feature store
- Apache Spark
- Kubeflow Pipelines
- MLflow
- Kubernetes
- model monitoring
- data drift
- online inference
- A/B testing
Mistakes to avoid
- Writing a profile paragraph for this layout. Catalyst drops it, so the key line belongs in the headline.
- Offline accuracy as the only result, with nothing about latency, cost or an online test.
- Notebook experiments described as if they were production systems.
- Every deep learning framework you have imported once. Keep the ones you have trained and served models in.
How the sample PDF reads in text extractors
We exported this sample with Resumity's PDF exporter in Chrome on 2026-10-29 and read the file back with three open-source text extractors. This shows whether software that turns a PDF into text can recover it; it is not a test of any commercial applicant tracking system. How we test templates.
| Extractor | Name, email, phone | Headings | Bullets intact |
|---|---|---|---|
| pdf.js 4.10.38 | All found | 4 of 4 | 8 of 8 |
| pdftotext (Xpdf) 4.00 | All found | 4 of 4 | 8 of 8 |
| pypdf 6.13.0 | All found | 4 of 4 | 8 of 8 |
Frequently asked questions
How is a machine learning engineer resume different from a data scientist's?
It leans on systems: serving, pipelines, retraining, monitoring and cost. A data scientist resume leans on questions answered and decisions changed. Many people do both; let the bullets show where your weight sits.
Should I list research papers on an ML engineer resume?
List one or two in an extra section if they relate to the models you would build. For most engineering roles, a production system you run outweighs a paper.
Is an AWS machine learning certification worth adding?
It helps most when you are new to ML engineering or to AWS. At senior level it is a small extra; AWS lists the Machine Learning Engineer - Associate as valid for three years, so keep the date on it.
How do I show production experience from a small company?
Describe the whole path you owned: data, training, deployment, monitoring. Small teams often mean one engineer did all of it, which is strong evidence if you say so plainly.
Should LLM work go on a machine learning engineer resume?
Yes, if you built or served it: fine-tuning, retrieval, evaluation or inference optimisation. Describe it with the same serving and quality numbers as any other model.
Will applicant tracking systems read this Catalyst PDF?
The sample PDF read back with its contact details, all 4 section headings and all 8 bullets intact in three open-source text extractors (pdf.js, pdftotext and pypdf). Commercial systems such as Workday, Greenhouse or Taleo were not tested, and each employer configures its own.
Guides and templates for this resume
Sources, checked 29 October 2026
We opened every page below on that date. Pay, rules and requirements change, so check the source before you rely on a detail.
- Amazon Web Services: AWS Certified Machine Learning Engineer - Associate (exact certification name; exam MLA-C01 recommends at least 1 year with Amazon SageMaker and other AWS ML services; certification valid for 3 years)
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