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Machine Learning Engineer resume example

Machine learning engineers are hired to get models into production and keep them working there. Your resume should show the systems around the model: data pipelines, training jobs, serving, latency, monitoring and cost. Accuracy numbers help, but only next to what the model did for users or the business.

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Why this resume works

  • Every bullet starts with a verb and ends with a result, most with a number.
  • The skills a machine learning engineer job post asks for appear in the Skills section and again inside the bullets.
  • A single, standard layout (Split Header) that applicant tracking systems read top to bottom.
  • Standard headings (Summary, Work Experience, Education, Skills) so the ATS files each part correctly.

Jacob Weiss

Machine Learning Engineer

jacob.weiss@email.com

(412) 555-0193

Pittsburgh, PA

linkedin.com/in/jacobweiss

github.com/jacobweiss

Professional Summary

Machine learning engineer with 5 years of experience building and serving recommendation and search models for an e-commerce marketplace with 9 million monthly users. Owns the path from feature pipelines to low-latency serving on Kubernetes, and has shipped ranking models that raised add-to-cart rate in A/B tests. Strong in Python, PyTorch, Spark and MLOps.

Work Experience

Machine Learning Engineer II

2022 – Present

Kartly Commerce

  • Built and shipped a two-tower retrieval model and a gradient-boosted re-ranker for product search; the A/B test showed a 7% rise in add-to-cart rate.
  • Served models with Triton on Kubernetes at 3,000 requests a second with p99 latency under 40 ms.
  • Moved training from notebooks to scheduled Airflow and Kubeflow pipelines with MLflow tracking, so models retrain weekly instead of quarterly.
  • Added drift and data-quality monitoring that caught a broken price feature within an hour instead of days.

Data Engineer

2020 – 2022

Vaani AI Labs

  • Built Spark pipelines that prepare 2 TB of speech and text data a week for model training.
  • Created the feature store tables used by 4 data scientists, ending duplicated feature code.
  • Cut the nightly pipeline's cloud cost by 35% by moving to spot instances and partitioned Parquet.

Education

BS, Computer Science

2016 – 2020

University of Pittsburgh, Pittsburgh, PA

Skills

Machine learning: PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, ranking and retrieval, embeddings, A/B testing

MLOps and data: Python, Spark, Airflow, Kubeflow, MLflow, Feast, Triton Inference Server, Docker, Kubernetes, GCP (Vertex AI, BigQuery)

Certificates

Google Cloud Professional Machine Learning Engineer, Google Cloud

2024

Example machine learning engineer resume · Split Header template · made-up person

Machine Learning Engineer resume summary examples

Two or three lines at the top: your years of experience, your strongest skills, and the result you are known for.

This example

Machine learning engineer with 5 years of experience building and serving recommendation and search models for an e-commerce marketplace with 9 million monthly users. Owns the path from feature pipelines to low-latency serving on Kubernetes, and has shipped ranking models that raised add-to-cart rate in A/B tests. Strong in Python, PyTorch, Spark and MLOps.

Entry level

Machine learning engineer and recent M.Tech graduate with a thesis on efficient text classification. Deployed a fine-tuned BERT model behind a FastAPI service during a 6-month internship, and writes tested Python with Docker and basic Kubernetes.

Experienced

Staff machine learning engineer with 11 years in ML systems for ads and payments. Designed the company's training and serving platform used by 40 data scientists, cut inference cost by 60% through model distillation and batching, and leads a team of 7 ML engineers.

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Machine Learning Engineer resume bullet points

Use them as a pattern, not a copy: swap in your own numbers. If you don't have a number, describe the before and after.

  • Shipped a search re-ranking model that raised add-to-cart rate by 7% in an A/B test.

  • Served models on Kubernetes at 3,000 requests a second with p99 latency under 40 ms.

  • Moved training from notebooks to weekly Airflow and Kubeflow pipelines with MLflow tracking.

  • Added drift monitoring that caught a broken feature within an hour instead of days.

  • Cut GPU inference cost by 45% with quantization and dynamic batching.

  • Built a feature store that 4 data scientists share, ending duplicated feature code.

  • Distilled a large language model into a smaller one for intent detection, keeping 97% of its accuracy at a tenth of the latency.

  • Wrote the model rollout checklist (shadow traffic, canary, rollback) used for every launch.

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Skills and ATS keywords for a machine learning engineer resume

Skills that often appear in machine learning engineer job posts. Add only the ones you really have, in the same words the post uses, and check every job post for its own.

  • Machine learning
  • MLOps
  • Python
  • PyTorch
  • TensorFlow
  • scikit-learn
  • Model deployment
  • Model serving
  • Feature engineering
  • Feature store
  • Kubernetes
  • Docker
  • Airflow
  • Kubeflow
  • MLflow
  • Spark
  • A/B testing
  • Recommendation systems
  • NLP
  • Model monitoring
Cover letter examples →See which keywords your resume is missing for a real job →

How to write a machine learning engineer resume

  1. 01

    Show the system, not just the model

    A machine learning engineer is judged on what happens after training: pipelines, serving, latency and monitoring. Give these at least as much space as model accuracy.

  2. 02

    Tie models to a business result

    "AUC 0.91" means little to most readers. "Raised add-to-cart rate by 7% in an A/B test" shows the model mattered.

  3. 03

    Give serving numbers

    Requests per second, p99 latency and cost per thousand predictions prove you've run models under real load.

  4. 04

    Name your MLOps stack

    Airflow, Kubeflow, MLflow, Vertex AI, SageMaker, Feast or Triton. Teams want to know you can work inside their tooling from week one.

Machine Learning Engineer resume, section by section

Header

Jacob's header includes GitHub because ML engineering teams like to see real code. If your public work is thin, a link to a write-up or talk about a production system works just as well.

Summary

It names the domain (recommendation and search), the scale (9 million monthly users), the part of the lifecycle he owns (feature pipelines to low-latency serving) and a result from an A/B test. The last line lists his core stack.

Work experience

His bullets move through the lifecycle: model and business result, serving under load, training pipelines, then monitoring. The data engineering role explains how he got to ML engineering and shows he understands the data side.

Skills

Two groups: machine learning methods and frameworks, then MLOps and data tools. Listing ranking and retrieval as a skill helps for recommendation roles, which often search for those words.

Certification

One cloud ML certification with the year. It supports the cloud tooling in his skills but isn't the main evidence, so it sits low on the page.

Education

One line with the institute and years. Add a thesis or publication only if it's relevant to the role, such as work on retrieval for a search team.

Entry-level to senior machine learning engineer resume bullet points

Junior ML engineer (0–2 years)

  • Wrapped a trained text classifier in a FastAPI service with Docker, serving 50 requests a second.
  • Wrote unit tests and data validation checks for 3 feature pipelines.
  • Automated weekly model evaluation reports that used to be built by hand in notebooks.
  • Reproduced a published baseline for product categorization and matched its accuracy.

ML engineer (3–5 years)

  • Own the fraud-scoring model from training to serving, scoring 2 million transactions a day.
  • Brought model retraining from manual quarterly runs to an automated weekly pipeline.
  • Cut p99 inference latency from 180 ms to 45 ms with ONNX export and request batching.
  • Set up shadow deployments so new models are compared on live traffic before launch.

Senior / staff ML engineer (6+ years)

  • Designed the shared training and serving platform used by 6 teams and 30 models.
  • Led the migration of all models to a single feature store, removing 12 duplicate pipelines.
  • Cut annual GPU spend by $150,000 through distillation, quantization and autoscaling.
  • Mentor 5 ML engineers and review every model launch plan for risk and rollback.

Mistakes to avoid on a machine learning engineer resume

  • Accuracy with no context

    "Achieved 94% accuracy" doesn't say whether that was good, on what data, or whether it reached users. Pair metrics with a baseline and a business result.

  • Leaving out production

    If your bullets end at training, a hiring manager will assume someone else did the hard part. Show serving, monitoring and retraining.

  • Listing every architecture you've read about

    Transformers, GANs, diffusion and reinforcement learning on one line invites questions you may not want. List what you've built and shipped.

  • Notebook-only projects

    A notebook proves you can train a model. A small service with tests, a Dockerfile and monitoring proves you can engineer one.

Key takeaways

  • Give production work as much space as modeling.
  • Tie each model to a measured business result.
  • Include latency, throughput and cost numbers.
  • Name the MLOps tools you run pipelines and serving with.

Questions about machine learning engineer resumes

What's the difference between a data scientist and a machine learning engineer resume?
A data scientist resume leads with analysis, experiments and modeling decisions. A machine learning engineer resume leads with production: pipelines, serving, latency, reliability and cost. Many people do some of both; weight your bullets toward the job you're applying for.
Should I list LLM and generative AI experience?
Yes, if you've built something real with it: fine-tuning, retrieval-augmented generation, evaluation or serving. Say what it did and how you measured quality, rather than only listing model names.
Do I need a master's degree for ML engineering roles?
It helps for research-heavy teams, but many ML engineers come from software or data engineering with a bachelor's degree. Shipped production systems usually count for more than the degree.
Should I include Kaggle competitions?
A strong ranking is worth one line in Projects, especially early in your career. Once you have production work, it matters much less, because Kaggle doesn't show serving or monitoring.
How do I show ML work if my company's models are confidential?
Describe the type of model, the problem, the scale and the measured result without sharing data, features or proprietary details. "A ranking model for product search serving 3,000 requests a second" is enough.

Sources

Duties, pay, education and job outlook for computer and information research scientists in the US: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook.

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