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Machine Learning Engineer Resume Example

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Wei Zhang - Profile Photo

Wei Zhang

Machine Learning Engineer

[email protected]+1 (555) 111-2233Palo Alto, CAClass C — Valid U.S. Driver's License

Professional Summary

Machine learning engineer with 4 years of experience building and deploying production ML systems at scale. Specialized in NLP, recommendation systems, and deep learning architectures, with models serving 10M+ predictions daily at sub-100ms latency. Published 3 papers at top-tier ML conferences including NeurIPS and ICML. Passionate about bridging the gap between research and production, with hands-on expertise in MLOps, distributed training, and real-time inference optimization.

Work Experience

Machine Learning Engineer

AI Platform Corp.

Jun 2022 - Present

  • Built recommendation engine serving 10M+ daily predictions with 99.5% availability and sub-80ms P99 latency
  • Developed end-to-end ML pipeline from data ingestion to model deployment using Kubeflow and MLflow, reducing deployment time from 2 weeks to 3 hours
  • Improved click-through rate by 25% through A/B tested model architecture changes across 4 product surfaces
  • Designed and implemented feature store serving 500+ features to 12 production models, reducing feature engineering duplication by 70%
  • Mentored 3 junior ML engineers and established model review process adopted across the ML platform team

ML Engineer

NLP Startup Inc.

Aug 2020 - May 2022

  • Fine-tuned BERT and GPT-2 models for document classification achieving 97% accuracy on production datasets spanning 15 languages
  • Built real-time text analysis pipeline processing 500K documents daily using Spark and Kafka with end-to-end latency under 200ms
  • Reduced model training time by 60% through distributed training on GPU clusters using Horovod and PyTorch DDP
  • Implemented model monitoring and drift detection system that triggered automatic retraining, maintaining model accuracy above 95% SLA

Data Science Intern

TechResearch Labs

May 2019 - Jul 2020

  • Developed image classification model with 94% accuracy for manufacturing defect detection, saving $2M annually in quality control costs
  • Created data augmentation pipeline increasing training dataset size by 5x using techniques including CutMix, MixUp, and synthetic generation
  • Published research paper on transfer learning methods at ICML workshop, receiving 50+ citations within the first year
  • Built interactive model explainability dashboard using SHAP and LIME, enabling non-technical stakeholders to interpret predictions

Education

M.S. Computer Science (Machine Learning)

Stanford University

2018 - 2020

B.S. Computer Science

University of Science and Technology of China

2014 - 2018

Courses & Certifications

AWS Certified Machine Learning - Specialty

Amazon Web Services

2023

Deep Learning Specialization

Coursera (DeepLearning.AI)

2021

TensorFlow Developer Certificate

Google

2020

Languages

Mandarin

Speaking: NativeListening: NativeWriting: Native

English

Speaking: FluentListening: FluentWriting: Fluent

Japanese

Speaking: IntermediateListening: IntermediateWriting: Basic

Skills

PythonPyTorchTensorFlowScikit-learnKubeflowMLflowSparkSQLDockerAWS SageMakerNLPDeep Learning

This is a sample resume. Customize it with your own experience using our free resume builder.

Tips for Your Machine Learning Engineer Resume

Show Production ML, Not Just Notebooks

Companies want engineers who deploy models, not just train them. Highlight model serving, latency metrics, monitoring, and MLOps pipeline experience.

Include Model Performance Metrics

Accuracy, F1, AUC-ROC, latency, and throughput numbers show your models actually work at scale. 'Sub-80ms P99 latency serving 10M predictions/day' is powerful.

Mention the Full ML Lifecycle

Data collection, feature engineering, model training, evaluation, deployment, monitoring. Show you understand the complete pipeline, not just the modeling step.

List Publications and Open Source

ML engineering values research contributions. If you have papers, conference talks, or significant open-source contributions, include them prominently.

Key Skills for a Machine Learning Engineer Resume

PythonPyTorch/TensorFlowML Pipeline DevelopmentModel DeploymentMLOps (Kubeflow/MLflow)NLP/Computer VisionDeep LearningDistributed TrainingFeature EngineeringA/B TestingAWS SageMakerData Engineering

Frequently Asked Questions

What should a machine learning engineer resume include?

An ML engineer resume should highlight production ML systems, model performance metrics, deployment pipeline experience, and programming skills (Python, PyTorch/TensorFlow). Include latency, throughput, and business impact metrics from deployed models.

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

ML engineer resumes emphasize production systems, deployment, MLOps, and engineering practices. Data scientist resumes focus more on analysis, experimentation, and statistical methods. ML engineers build systems; data scientists build models.

Do I need a graduate degree for ML engineering?

Many ML engineer roles prefer a master's or PhD, but it's not always required. Strong project experience, open-source contributions, and demonstrated ability to deploy production ML systems can compensate for formal education.

Can I create an ML engineer resume for free?

Yes. NoBsResume is 100% free. Choose an ATS-friendly template, showcase your ML projects and production experience, and download as PDF. No signup required.

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