Machine Learning Engineer Resume Example
Professional Machine Learning Engineer resume example with ATS-optimized template. Learn how to highlight your ML model development and deployment expertise.
Last Updated: 2026-02-02 | Reading Time: 8-10 minutes
Quick Stats
Machine Learning Engineer Resume Example
David Okafor
david.okafor@email.com | (555) 345-7890 | San Francisco, CA
linkedin.com/in/davidokafor
Professional Summary
Machine Learning Engineer with 5+ years of experience designing, training, and deploying production ML systems at scale. Built recommendation and NLP models serving 10M+ users. Expertise in PyTorch, MLOps, and distributed training. Published author at NeurIPS and ICML.
Experience
- •Built content recommendation engine serving 10M+ daily active users, increasing watch time by 28%
- •Designed and deployed real-time NLP pipeline processing 50K+ text documents per hour with 94% accuracy
- •Reduced model training time by 70% through distributed training on multi-GPU clusters using PyTorch DDP
- •Led MLOps initiative implementing model versioning, A/B testing, and automated retraining pipelines
- •Developed computer vision model for defect detection achieving 97% precision on manufacturing data
- •Built end-to-end ML pipeline from data ingestion to model serving using Kubeflow and TensorFlow Serving
- •Implemented feature store serving 200+ features to 15+ models across 3 product teams
Education
Technical Skills
PyTorch • TensorFlow • Python • Scikit-learn • Kubeflow • MLflow • Ray • CUDA • Spark • SQL
Certifications
- Google Professional Machine Learning Engineer
- NVIDIA Deep Learning Institute Certification
Why This Resume Works:
- Quantified achievements with specific metrics
- Keywords match common job descriptions
- Clean, ATS-compatible formatting
- Strong action verbs throughout
How to Write a Machine Learning Engineer Resume
Professional Summary
Highlight the types of ML systems you have built, the scale of serving, and publications or patents. Differentiate yourself from data scientists by emphasizing production deployment.
Work Experience
Focus on model performance metrics (accuracy, precision, recall, F1), serving scale, and latency requirements. Show the full lifecycle: research, development, deployment, monitoring.
Skills Section
Lead with ML frameworks (PyTorch, TensorFlow), then MLOps tools, cloud platforms, and programming languages. Include specific ML techniques you specialize in.
Action Verbs for Machine Learning Engineers
Machine Learning Engineer Resume Keywords
These keywords appear most frequently in Machine Learning Engineer job descriptions. Include relevant ones in your resume:
Technical Keywords
Deep LearningNatural Language ProcessingComputer VisionRecommendation SystemsModel ServingFeature EngineeringDistributed TrainingTransfer LearningTransformer ModelsMLOpsModel MonitoringReinforcement LearningIndustry Keywords
Production MLAI/ML InfrastructureModel DeploymentReal-Time InferenceBatch PredictionExperimentation PlatformFeature StoreModel RegistryAutoMLResponsible AITools & Technologies
PyTorchTensorFlowScikit-learnKubeflowMLflowWeights & BiasesRayHugging FaceONNXTensorRTDockerKubernetesAWS SageMakerVertex AICommon Mistakes to Avoid
Confusing ML Engineer with Data Scientist on your resume
Emphasize production systems, deployment, and engineering. Show you can take models from research to serving millions of users
Not including model performance metrics
Always include accuracy, precision, recall, AUC-ROC, latency, and throughput metrics for models you deployed
Omitting MLOps and infrastructure experience
Highlight CI/CD for ML, model monitoring, automated retraining, and feature store management. These are core MLE responsibilities
Not mentioning scale of serving
Include daily active users, requests per second, and data volumes your ML systems handle
Machine Learning Engineer Resume FAQs
What is the difference between a Data Scientist and an ML Engineer?
ML Engineers focus on building production systems: model deployment, serving infrastructure, MLOps, and scalability. Data Scientists focus more on analysis, experimentation, and model development. Highlight your engineering and production deployment skills.
Should I include research publications on my resume?
Yes. Publications at top venues (NeurIPS, ICML, CVPR) are highly valued. Include them in a Publications section with citation counts. Even preprints on arXiv demonstrate research capability.
How important is MLOps for ML Engineer roles?
Critical. Companies want engineers who can deploy, monitor, and maintain ML systems in production. Include experience with model versioning, A/B testing, feature stores, and automated retraining pipelines.
Is a Master's or PhD required for ML Engineering roles?
A Master's is common but not always required. Strong engineering skills with practical ML deployment experience can substitute. However, for research-heavy roles, advanced degrees are preferred.
Should I include Kaggle or competitive ML experience?
Yes, especially notable placements (top 5%, medal wins). But balance competitive ML with production experience. Companies care most about deploying and maintaining ML systems at scale.
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Last updated: 2026-02-02 | Written by JobJourney Career Experts