Machine Learning Engineer Resume Example and Template
A strong Machine Learning Engineer resume demonstrates expertise building and deploying production models, not just training notebooks. Show models you've shipped, the business value they've delivered, and your capability across the full ML lifecycle from data preparation to monitoring.
Recruiters seek engineers who understand both deep learning frameworks and software engineering practices. Mention production deployments, model performance metrics, and systems you've built at scale.
A complete, realistic Machine Learning Engineer resume, laid out in an ATS-tested template. The candidate is illustrative: the name, employers and figures were written for this example and identify no real person, so you can copy its structure and wording freely.
Updated September 2026
Also searched as
Senior Machine Learning Engineer
Arjun Sharma: Machine Learning Engineer
San Jose, California, United States
Summary
Production-focused Machine Learning Engineer with 6 years building and deploying deep learning models. Expert in PyTorch, TensorFlow, and computer vision. Deployed 8 production models serving 2.1M users. Led computer vision project improving image classification accuracy from 84% to 97.3% on 6M image dataset.
Work Experience
Senior Machine Learning Engineer, VisionAI Technologies
Jun 2022 – Present. 110-person computer vision startup providing automated image analysis for e-commerce and logistics companies
- Led computer vision project improving product image classification from 84% to 97.3% accuracy using transfer learning and data augmentation on 6M image dataset serving 2.1M users.
- Deployed 5 deep learning models to production using TensorFlow Serving and NVIDIA GPU infrastructure, handling 180K daily inference requests with sub-100ms latency.
- Implemented MLOps pipeline using Airflow and DVC, automating model training, evaluation, and deployment, reducing time to deploy new model versions from 2 weeks to 4 hours.
- Mentored 3 junior ML engineers on PyTorch best practices, production model optimization, and debugging strategies for deep learning systems.
Machine Learning Engineer, DataML Solutions
Jan 2020 – May 2022. 65-person AI/ML consulting firm building models for finance, healthcare, and manufacturing clients
- Built and deployed 3 production deep learning models for healthcare clients, including diagnostic assistance system achieving 92% sensitivity and 94% specificity on 1.2M medical images.
- Developed time-series forecasting model using LSTM networks for financial client, predicting stock price movements with 78% directional accuracy on 10-year historical data.
- Implemented model monitoring and drift detection system using TensorFlow and Prometheus, alerting on model performance degradation within 2 hours of occurrence.
Education
Indian Institute of Technology Delhi
Master of Science, Computer Science. Jul 2018 – Dec 2019
Delhi University
Bachelor of Technology, Computer Science. Jul 2014 – May 2018
Skills
PyTorch, TensorFlow, Computer Vision, Deep Learning, Python, CUDA, Model Deployment, MLOps, Data Pipeline, Kubernetes, A/B Testing, SQL
Languages
English, Hindi
Why this look works
Machine Learning demands technical sophistication and forward-thinking innovation. A modern, technical layout conveys deep expertise. Neural network blues and cutting-edge typography signal both advanced capability and innovation mindset, appealing to AI-first companies.
Resume tips for this job
- Lead with production models and their business impact, not research papers. 'Deployed 8 production models serving 2.1M users' beats 'experienced with deep learning'. Deployments prove you ship, not just experiment.
- Quantify model performance and business outcomes. Say 'improved image classification accuracy from 84% to 97.3%' or 'diagnostic system achieving 92% sensitivity and 94% specificity'. Numbers prove your models work.
- Show full lifecycle capability. Mention data preparation, model training, deployment, monitoring, and retraining. ML engineers who think beyond training are increasingly valuable.
- Demonstrate production thinking. Mention latency targets you've hit, inference infrastructure you've designed, or model monitoring systems you've built. Production ML is different from notebooks.
Common questions
Should I include academic research or focus only on production work?
Lead with production work and business impact. Academic research can complement production experience, but shipping models to users is more valuable to employers. If you've published, mention it briefly, but your production portfolio matters more.
I have strong PyTorch experience but limited TensorFlow knowledge. Is that a blocker?
No. Deep learning frameworks share principles; learning TensorFlow is straightforward if you know PyTorch deeply. Mention PyTorch expertise confidently and show you understand deep learning fundamentals beyond syntax. Employers value depth in one framework over shallow breadth.
How do I explain models that underperformed or didn't ship?
Discuss learning: 'Explored RNN approach for time-series forecasting, achieving 62% accuracy, then pivoted to ensemble methods improving to 78%.' Shows experimentation discipline. Unsuccessful experiments teach more than successes; frame them as part of your process.