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Machine Learning Engineer Resume Example (2026)

By FindAJobAI team Updated How we built this page

Machine learning engineering ads now split into classic ML, deep learning and LLM work, and they care about production: serving, latency, monitoring and retraining. A machine learning engineer resume should say which kind of work you've shipped and how it performed online, not only offline.

The example below is an ML engineer with six years, from a fraud model in an insurer's first production deployment to search ranking and a retrieval-augmented support assistant at a marketplace. Each bullet carries an online result, a latency figure or an evaluation score.

Machine Learning Engineer resume example (fictional candidate Julia Novak)
Fictional example · Julia Novak, Machine Learning Engineer

Machine Learning Engineer resume template

This example as a one-page template: one column, standard headings and plain text, the format applicant tracking systems read most reliably.

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After you sign up, Create my CV builds your own resume in this same one-column layout, from your real experience.

Mid-level machine learning engineer resume example

A complete one-page example for a fictional machine learning engineer: every bullet pairs an action with what changed because of it.

Example resume · fictional person

Julia Novak

Machine Learning Engineer

San Diego, CA · [email protected] · (619) 555-0166 · LinkedIn · GitHub

Summary

Machine learning engineer with 6 years of experience taking ranking, classification and LLM systems to production. Trained and served a PyTorch search model that raised conversion by 8% across 12 million monthly queries, at p99 under 45 ms. Builds evaluation sets before models.

Experience

Machine Learning Engineer, Coastline Marketplace

San Diego, CA · Jun 2022 – Present

  • Trained and deployed a PyTorch two-tower retrieval model for product search, raising search conversion by 8% across 12 million monthly queries.
  • Built real-time serving on Kubernetes with Triton and a Redis feature cache, keeping p99 latency under 45 ms at 3,000 requests a second.
  • Shipped a retrieval-augmented (RAG) support assistant over 20,000 help articles, guided by a 600-question evaluation set.
  • Set up MLflow tracking and automated weekly retraining, cutting model refresh from a manual 2-week process to 1 day.

Data Scientist, Pacific Crest Insurance

San Diego, CA · Aug 2020 – May 2022

  • Built a claims-fraud classifier in LightGBM that flagged 2x the confirmed fraud at the same review capacity.
  • Moved the model from notebooks to a FastAPI service with data-drift monitoring, the team's first model in production.
  • Wrote Spark feature pipelines over 40 million historical claims.

Skills

  • Machine learning: PyTorch, scikit-learn, LightGBM, Hugging Face Transformers, embeddings and vector search
  • LLMs: RAG, evaluation design, fine-tuning (LoRA), hosted and open-weight models
  • MLOps: MLflow, Kubeflow, Docker, Kubernetes, Triton Inference Server, Airflow
  • Languages and data: Python, SQL, Spark, FAISS, Redis

Education

  • M.S. Computer Science (Machine Learning), University of California San Diego, 2020

Fictional example by the FindAJobAI team: the person, employers and numbers are invented. Keep the structure; use your own work and numbers you can explain in an interview.

Entry-level machine learning engineer resume example

A master's graduate with one industry internship and a research job. The bullets prove production habits early: latency under load, an evaluation step the team kept, and a training job moved off a laptop.

Entry-level example resume · fictional person

Wei Zhang

Machine Learning Engineer

Pittsburgh, PA · [email protected] · (412) 555-0190 · LinkedIn · GitHub

Summary

Machine learning engineer with an M.S. in Computer Science and an internship serving recommendation models for a streaming service. Writes production Python, trains models in PyTorch and packages them with Docker and FastAPI. Most interested in retrieval, ranking and model evaluation.

Education

  • M.S. Computer Science (Machine Learning), Carnegie Mellon University, 2026
  • B.S. Computer Science, University of Pittsburgh, 2024

Experience

Research Assistant, Language Technologies, Carnegie Mellon University

Pittsburgh, PA · Sep 2025 – May 2026

  • Fine-tuned 4 open-weight language models with LoRA for clinical note classification, raising macro-F1 from 0.71 to 0.79.
  • Built the evaluation harness of 1,200 labeled examples now used by 5 lab members.

Machine Learning Engineering Intern, Riverstream Media

Remote (US) · May 2025 – Aug 2025

  • Wrote a FastAPI service that serves the home-screen recommendation model, holding p95 latency under 60 ms at 500 requests a second in load tests.
  • Added offline evaluation for 3 candidate models with recall@20 and catalog coverage, a step the team now runs before every release.
  • Moved the training job into a Docker image on a nightly Kubernetes CronJob, ending manual retraining.

Skills

  • Machine learning: PyTorch, scikit-learn, Hugging Face Transformers, LoRA fine-tuning
  • Serving: FastAPI, Docker, Kubernetes (basic), ONNX Runtime
  • Languages: Python, SQL, C++

Projects

  • Semantic search over course reviews (Python, sentence embeddings, FAISS): indexed 80,000 reviews with hybrid ranking and a 200-query test set, reaching nDCG@10 of 0.64.

Senior machine learning engineer resume example

A staff engineer whose impact is the platform and the standards other engineers build on. Revenue, latency and cost figures sit beside the launch process, because at this level judgment counts as much as models.

Senior example resume · fictional person

Fatima Al-Sayed

Staff Machine Learning Engineer

Mountain View, CA · [email protected] · (650) 555-0167 · LinkedIn · GitHub

Summary

Staff machine learning engineer with 12 years building search, ads and LLM systems, leading the ML platform for 30 engineers. Took ads ranking from batch to real-time serving, adding $40M a year in revenue, and set up the evaluation and cost controls for 5 LLM features in production.

Experience

Staff Machine Learning Engineer, Brightline Commerce

Mountain View, CA · Mar 2021 – Present

  • Lead the ML platform used by 30 engineers: feature store, training pipelines on Ray, and serving on Kubernetes with Triton.
  • Moved ads ranking from daily batch scores to real-time inference at 20,000 requests a second, adding $40M a year in ad revenue.
  • Set up LLM evaluation (golden sets, human review, online metrics) and cost budgets for 5 features, cutting inference spend per request by 60%.
  • Defined the model launch review (offline metrics, shadow traffic, canary, rollback plan) used for every launch since 2022.

Senior Machine Learning Engineer, Quarry Search

San Francisco, CA · Jan 2017 – Feb 2021

  • Built a learning-to-rank model for site search over 50 million documents, raising first-result click-through by 9%.
  • Cut ranking-model training from 30 hours to 4 with distributed GPU training.
  • Built drift dashboards that caught 3 silent feature breaks before they hurt rankings.

Software Engineer, Machine Learning, Tidepool Analytics

Oakland, CA · Jul 2014 – Dec 2016

  • Shipped the company's first production model, a spam classifier for 2 million user messages a day.
  • Wrote the Spark feature pipelines that 4 later models reused.

Skills

  • ML systems: Ranking, retrieval, LLM evaluation, feature stores, online experimentation
  • Frameworks: PyTorch, TensorFlow, Ray, Triton Inference Server, vLLM
  • Infrastructure: Kubernetes, Spark, Kafka, GCP, AWS
  • Leadership: Technical direction, design reviews, mentoring, cost governance

Education

  • M.S. Electrical Engineering, Stanford University, 2014

How to write a machine learning engineer resume

Five steps, in the order a recruiter reads the page. The format around them stays plain for every job: one column, standard headings, newest role first.

  1. Header: name, target title and links

    Use Machine Learning Engineer or the ad's variant (ML Engineer, Applied ML Engineer, AI Engineer), and link a GitHub with a served model or a well-documented project. Add your focus after the title, such as Ranking and Retrieval, when it fits the ad.

  2. Summary: what you do and your best proof

    Say what kind of ML you ship (classic, deep learning or LLM), the scale it runs at and one online result. Mention serving or MLOps, since that is what separates this role from data science.

  3. Experience: newest first, a result in every bullet

    Pair each model with how it reached users and how it did there: latency, throughput, cost and the A/B result. Offline metrics go in the same bullet as the online outcome, not instead of it.

  4. Skills: grouped, in the ad's words

    Group frameworks, serving and MLOps tools, data tools and languages. Keep to tools you used in production or serious projects, because ML interviews dig into each one.

  5. Education and certifications

    List degrees with field and year, and add papers or patents in a Publications section if the job leans toward research. For LLM roles, a public evaluation or fine-tuning project can weigh as much as a course.

Skills to put on a machine learning engineer resume

Hard skills are the tools and methods of the job; soft skills are how you work with people. Each comes with one way to prove it, because a skill named only in a list counts for little. Add the ones you really have.

Hard skills

PyTorch
Name a model you trained with it and the online metric it moved.
Python
Show production code (services, pipelines, tests), not only notebooks.
Model serving
Give latency, throughput and the serving stack: Triton, TorchServe, vLLM or FastAPI.
MLOps
Describe how your models are tracked, retrained and rolled back, and with which tools (MLflow, Kubeflow).
Feature pipelines
Mention a feature pipeline other models reused, or a training-serving skew you fixed.
LLMs and RAG
Say how you measured answer quality, the eval set size and the result, not just which model you called.
Evaluation design
Name the offline metrics and test set, and how they compared with online results.
Kubernetes and Docker
Show that you deployed your own models, with autoscaling, GPUs or canary releases.
Distributed training
Quote training time or cost before and after, with the framework (Ray, DeepSpeed, PyTorch DDP).
Vector search
Name the index (FAISS or a vector database), its size and the recall or latency reached.
Spark and SQL
Mention the size of the training data you assembled and how you built it.
Monitoring and drift detection
Give one problem your monitoring caught and how quickly.

Soft skills

Pragmatism
Show a case where a simpler model or heuristic won, and you shipped it.
Cross-team communication
Name the product or data partners you worked with and the decision you reached together.
Ownership
Mention a model you run in production, including on-call or incident follow-up.
Experiment discipline
Show that you compared against a baseline and held out traffic before claiming a win.
Mentoring
Count the engineers you onboarded to the ML stack or reviewed regularly.
Cost awareness
Put a number on GPU or inference spend you cut.
Technical writing
Mention design docs or launch reviews you wrote and who relied on them.

What employers ask for in machine learning engineer job ads

The skills named most often in the 478 newest machine learning engineer job ads we track (out of 3,612 live postings), as the share of ads that mention each:

  1. 1Python81%
  2. 2LLMs69%
  3. 3AWS36%
  4. 4RAG35%
  5. 5CI/CD30%
  6. 6PyTorch28%
  7. 7Observability27%
  8. 8Azure26%

Add only the ones you really have, in Skills and in a bullet that shows how you used them. The full list is on the Machine Learning Engineer resume keywords page.

From live job ads · computed November 2, 2026 · refreshed daily

Machine Learning Engineer resume bullet examples: weak vs strong

The same work, written two ways. The strong version names the tool, the scope and a result; the numbers are examples, yours must be real.

  1. Weak: Developed machine learning models.

    Strong: Trained a LightGBM ranking model on 90 days of click data that lifted add-to-cart from search by 5% in an A/B test.

    Model, data and an online result, not just an offline score.

  2. Weak: Deployed models to production.

    Strong: Packaged models as Docker images served by Triton on Kubernetes with canary releases, taking deploys from a week of hand-offs to 2 hours.

    Shows real serving work and the speed it gave the team.

  3. Weak: Worked on LLM applications.

    Strong: Built a RAG pipeline over 50,000 policy documents with hybrid search and a 400-question eval set, raising correct answers from 62% to 84%.

    LLM work with an evaluation, which hiring managers ask about first.

  4. Weak: Monitored model performance.

    Strong: Added drift and calibration monitoring that caught a broken upstream feature within a day, before conversion dropped.

    Monitoring proven by a problem it caught.

  5. Weak: Improved model accuracy.

    Strong: Added session features and hard-negative sampling, raising recall@10 on the held-out set from 0.61 to 0.72.

    Names the technique and a metric with its definition.

  6. Weak: Reduced inference costs.

    Strong: Distilled a 7B-parameter model into a 1B model for intent classification, cutting GPU cost by 80% for a 1-point accuracy drop.

    A clear trade-off, stated honestly.

15 more machine learning engineer resume bullet examples

Grouped by area of the job, so you can find the ones closest to your work. Keep the shape (action, scope, result) and replace the invented numbers with your own.

Training and modeling

  • Trained a multilingual intent classifier on 2 million support messages, routing tickets in 9 languages at 91% accuracy.
  • Added hard-negative mining and in-batch negatives to the retrieval model, raising recall@50 from 0.68 to 0.81.
  • Shrank a vision model 4x with quantization and pruning, keeping accuracy within 1 point so it runs on in-store cameras.
  • Built a time-aware validation split that exposed leakage in the old setup, bringing offline and online metrics back into line.

Serving and infrastructure

  • Served a 7B-parameter model with vLLM on 4 GPUs at 40 requests a second, cutting cost per 1,000 requests from $1.90 to $0.45.
  • Built a shadow deployment path that mirrors live traffic to new models, catching 2 regressions before any user saw them.
  • Moved batch scoring for 30 million users from one machine to Spark, cutting the daily job from 7 hours to 50 minutes.
  • Put a Redis cache in front of the feature store, cutting p99 inference latency from 120 ms to 35 ms.

LLM and retrieval systems

  • Built a RAG assistant over 12,000 internal documents with hybrid search and citation checks, answering a third of employee questions without a ticket.
  • Created a 900-example evaluation set with graded answers and automated scoring, used to compare 6 model and prompt versions.
  • Added guardrails for personal data and off-topic requests to a customer chatbot, after which escalations for unsafe answers fell to near zero.

MLOps and monitoring

  • Set up MLflow tracking and a model registry for 15 models, so every production version traces back to its data and code.
  • Added drift alerts on the 20 most important features, flagging a broken upstream join within 4 hours.
  • Automated weekly retraining with validation gates, so a model ships only when it beats the current one on the holdout set.
  • Wrote the on-call runbook for 8 production models, cutting median rollback time for a bad model from 2 hours to 10 minutes.

Machine Learning Engineer resume summary examples

Two or three lines on what you do and your strongest proof. Start from the one closest to your level and use your own facts.

Entry-level
Machine learning engineer with an M.S. in computer science and a thesis on efficient transformer fine-tuning. Interned on a recommendations team, shipping a Spark feature pipeline to production. Strong in PyTorch, Python and SQL.
Mid-level
Machine learning engineer with 5 years of experience building and serving fraud and credit models on AWS SageMaker. Owns the training, deployment and monitoring pipeline for 4 models that score every card transaction in real time.
Senior
Senior ML engineer with 10 years of experience leading a team of 5 that builds LLM and recommendation systems. Designed the evaluation framework, serving platform and cost controls for 6 production models.

Common machine learning engineer resume mistakes

Each one is easy to make and quick to fix before you apply.

  1. Only offline metrics

    Accuracy on a test set doesn't show the model worked for users. Add the online result, or the reason it never shipped.

  2. Notebook-only experience

    If your models stayed in notebooks, the resume reads as data science. Show code you deployed, scheduled or handed to engineers to run.

  3. Calling an API and calling it LLM engineering

    Hiring managers ask how you measured quality, handled failures and controlled cost. Put those details in the bullet, with your evaluation set.

  4. No latency or cost numbers

    Serving is where ML engineering lives. Give p95 or p99 latency, throughput and cost per request wherever you can.

  5. Every framework you have tried

    A long framework list invites deep questions on each. Keep the ones you'd be comfortable defending in a design round.

ATS tips for a machine learning engineer resume

Applicant tracking systems turn your file into text that recruiters search, mostly for the skills and titles in the ad. They rarely reject a resume on their own; one that parses badly or misses the ad's words just doesn't come up.

  • Say whether your work is classic ML, deep learning or LLM work, and which of it reached production; ML ads now separate them clearly.
  • Name frameworks and serving tools as the ad does (PyTorch, TensorFlow, Triton, vLLM, Ray, MLflow).
  • Report online results (A/B lifts, latency, cost) alongside offline metrics such as AUC or recall.
  • For LLM work, describe how you measured quality, not only which model you called.
  • Put papers, patents or open-source models in a short Publications or Projects section as plain text.

For the format itself, run the free ATS resume checklist.

Everything for Machine Learning Engineers

Other resume examples

Machine Learning Engineer resume FAQ

What's the difference between a machine learning engineer and a data scientist resume?

An ML engineer resume emphasizes production: training pipelines, serving, latency, monitoring and scale. A data scientist resume emphasizes analysis, experiments and the business decisions models support.

How do I show LLM experience on a resume?

Describe the system you built (RAG, fine-tuning or agents), the data it used, how you evaluated it and its result in production. A named evaluation set with a before-and-after score shows real depth.

Should I include research papers?

Yes, in a short Publications section if they relate to the role, one line each with the venue and year. For applied roles, production bullets should still come first.

How we built this page

The FindAJobAI team wrote the three example resumes, the bullets, the skills and the advice for machine learning engineer roles. The people, employers and numbers in the examples are invented; the structure, the tools and the way each bullet is built are the parts to copy.

The live skills section is based on 478 machine learning engineer job ads analysed, the newest we track, with the same deterministic skill matcher as our in-demand skills report: no AI model reads the postings. It was last computed on November 2, 2026 and refreshes daily.

Last reviewed on November 1, 2026.