Machine Learning Engineer resume example

ML engineering resumes are judged on whether a model reached production, not on notebook accuracy alone. State the framework, the deployment path (SageMaker, TorchServe, custom API), and the inference latency or throughput it hit in production.

Distinguish this role clearly from data science by emphasizing serving infrastructure, monitoring for model drift, and MLOps pipeline ownership — the engineering half of the job, not just model training.

Grace Kim

Machine Learning Engineer

San Jose, CA grace.kim@email.com +1 (408) 555-0157 linkedin.com/in/gracekim github.com/gkim-ml

Summary

Machine learning engineer with 6 years shipping models to production at scale. Deployed a recommendation model serving 8M monthly users at sub-40ms p99 latency and built the MLOps pipeline that cut retraining-to-deploy time from 2 weeks to 2 days.

Experience

Machine Learning Engineer

Applied Cortex · San Jose, CA

Jun 2022 — Present

Deployed a PyTorch recommendation model via SageMaker with sub-40ms p99 inference latency, lifting click-through rate 16% for 8M monthly users.
Built an MLflow-based retraining pipeline with automated drift detection, cutting time from data refresh to production deploy from 2 weeks to 2 days.
Converted a computer-vision defect-detection model to ONNX for edge deployment, cutting inference cost per unit 55%.

ML Engineer

Delmore AI · San Jose, CA

Jul 2019 — May 2022

Built and served a TensorFlow NLP classification model handling 50K requests/hour via a Kubernetes-hosted TorchServe cluster.
Implemented feature-store infrastructure shared across 4 model teams, cutting duplicate feature-engineering work by an estimated 30%.
Ran shadow-mode A/B testing before full rollout, catching a data-leakage bug that would have inflated reported accuracy by 12 points.

Skills

PyTorch · TensorFlow · Python · MLflow · SageMaker · Docker · Kubernetes · ONNX · Airflow · NLP

Education

San Jose State University

M.S. Computer Science

2017 — 2019

Concentration: Machine Learning

Machine Learning Engineer · Technical Mono template · single-column, ATS-safe.

ATS keywords for a Machine Learning Engineer resume

Applicant tracking systems match your resume against the job description's vocabulary. Mirror the terms below that are true of you — ideally with a metric attached.

PyTorchTensorFlowPythonMLflowSageMakerDockerKubernetesModel DeploymentFeature EngineeringNLPComputer VisionONNXAirflowA/B Testing

What recruiters look for

A model that reached production, with an inference latency or throughput number.
The serving stack named explicitly (TorchServe, SageMaker, Triton) — not just the training framework.
Model monitoring or drift-detection ownership, showing post-deployment responsibility.
MLOps pipeline evidence: retraining automation, versioning, CI/CD for models (MLflow, Airflow).
A business metric the model moved, connecting engineering work to actual impact.

Before & after: one bullet

Weak

Trained and deployed machine learning models for the recommendation system.

Strong

Deployed a PyTorch recommendation model via SageMaker with sub-40ms p99 inference latency, lifting click-through rate 16% for 8M monthly users.

Common mistakes to avoid

Resume reads like a data scientist's — model accuracy only, no deployment or serving detail.
No inference latency or throughput number, leaving production readiness unclear.
Confusing 'trained a model' with 'shipped a model' — recruiters want the second.
Missing MLOps tooling (MLflow, model registries, CI/CD for retraining) that separates engineers from researchers.