Updated April 2026

Deep Learning Engineer
Resume Example

An ATS-optimized resume structure for deep learning engineers, featuring real model architectures, quantified training results, and production ML impact. Copy it, adapt it, land more interviews.

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ATS Score
89
Excellent
Keywords · Impact · Format
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Priya Raghavan

Seattle, WA  |  [email protected]  |  (555) 871-4302  |  linkedin.com/in/priyaraghavan  |  github.com/priyarag
Summary

Deep learning engineer with 5 years of experience designing and deploying neural network models for computer vision and NLP at scale. Led a team that shipped a real-time object detection pipeline processing 12M images/day in production. Skilled in model optimization, distributed training, and bridging research prototypes to production systems on GPU clusters.

Technical Skills
Deep Learning Frameworks: PyTorch, TensorFlow, JAX, Hugging Face Transformers, ONNX Runtime
Neural Network Architectures: CNNs, Transformers, GANs, U-Net, LSTM, Diffusion Models, ViT
Programming Languages: Python, C++, CUDA, SQL, Bash
GPU Computing & Infrastructure: NVIDIA A100/H100, TensorRT, DeepSpeed, AWS SageMaker, Docker, Kubernetes, MLflow, Weights & Biases
Experience
Senior Deep Learning Engineer, Percept AI Jan 2024 – Present
  • Designed a multi-task Transformer model for real-time object detection and segmentation, achieving 91.3% mAP on internal benchmarks while reducing inference latency by 40% using TensorRT optimization
  • Built a distributed training pipeline on 64 A100 GPUs with DeepSpeed ZeRO-3, cutting training time for a 7B parameter model from 14 days to 3.5 days
  • Led the migration of 6 production models from TensorFlow to PyTorch, establishing standardized model packaging with ONNX that reduced deployment time from 2 weeks to 3 days
  • Implemented a data flywheel system using active learning that automatically identified high-value training samples, improving model accuracy by 4.2% while reducing labeling costs by $120K/year
Deep Learning Engineer, NovaSight Labs Aug 2021 – Dec 2023
  • Developed a custom Vision Transformer (ViT) for medical image classification that achieved 96.8% accuracy on chest X-ray datasets, outperforming the previous CNN baseline by 3.1%
  • Optimized inference latency for a text generation model from 850ms to 120ms per request by applying quantization (INT8), knowledge distillation, and KV-cache optimization
  • Created an automated model evaluation framework with Weights & Biases that tracked 40+ experiments weekly, reducing time spent on manual benchmarking by 15 hours/week
  • Trained and fine-tuned large language models (LLMs) on domain-specific medical corpora using LoRA, achieving 22% improvement in clinical entity extraction F1 score over the base model
Education
M.S. Computer Science (Machine Learning), Carnegie Mellon University 2021
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Why This Resume Works

This resume scores well with ATS systems and hiring managers because it follows three principles:

1
Quantified model performance in every bullet

mAP scores, latency reductions, training speedups, cost savings. Every claim has a number behind it.

2
Specific architectures and tools named

Transformer, ViT, DeepSpeed ZeRO-3, TensorRT, LoRA. ATS keyword matching depends on exact technical terms.

3
Research-to-production narrative

Shows the full journey from training models to deploying them at scale. This is what hiring managers want to see.

Section-by-Section Breakdown

Summary

Lead with years of experience and your core DL domains (computer vision, NLP, generative AI). Include your biggest production-scale achievement and mention the infrastructure you work with. Keep it to 2-3 sentences. Recruiters want to see that you ship models, not just train them.

Technical Skills

Group skills into clear categories: frameworks, architectures, languages, and infrastructure. Deep learning roles require very specific keyword matching, so list exact framework names (PyTorch, not just "deep learning frameworks") and exact architecture names (Transformer, U-Net, not just "neural networks").

Tip: Mirror the exact terms from the job description. If they say "distributed training," include it. If they mention "CUDA," list it separately from C++.

Experience

Use this formula for every bullet point:

[Action verb] + [model/system you built] + [architecture/tool used] + [measurable result]

Start bullets with strong verbs: Designed, Trained, Optimized, Deployed, Implemented, Built, Reduced. Avoid "Worked on" or "Assisted with," which hide your individual contribution.

3-5 bullets per role. Lead with production impact, then model performance metrics.

Education

For deep learning engineers with 3+ years of experience, education goes last. Include your degree, specialization (Machine Learning, AI, Computer Vision), school, and year. Skip coursework unless you are early-career. If you have notable publications, consider a separate "Publications" section instead.

Key Skills for Deep Learning Engineer Resumes

Based on analysis of thousands of job postings, these are the most frequently required skills:

PyTorch TensorFlow Python CUDA Transformers CNNs Distributed Training Model Optimization Computer Vision NLP

Common Mistakes on Deep Learning Engineer Resumes

  • ⚠Listing only research metrics without production context. Saying "achieved 94% accuracy" means little without throughput, latency, or scale. Always tie model performance to real-world deployment numbers.
  • ⚠Using vague terms like "deep learning" without naming architectures. Write "Transformer," "U-Net," or "ResNet-50" instead of "neural network model." Specific names score higher with ATS and signal deeper expertise.
  • ⚠Focusing only on training and ignoring deployment. Companies want engineers who can ship models, not just run notebooks. Show experience with TensorRT, ONNX, model serving, and production monitoring.
  • ⚠Listing every paper you have read as a skill. Only include architectures and techniques you have hands-on experience implementing. Interviewers will ask you to explain trade-offs in detail.

How to Write a Deep Learning Engineer Resume That Gets Interviews

The best tech resumes prove you can ship working software that solves real problems. Hiring managers and ATS systems both look for specific technical skills matched to measurable outcomes.

1
Lead with your tech stack

Put your most relevant languages, frameworks, and cloud platforms in the first 3 lines. Engineering managers decide in seconds whether your stack matches their needs.

2
Quantify system impact

Instead of "worked on backend services," write "Built microservices handling 50K RPM with p99 latency under 100ms." Scale, uptime, and performance numbers show engineering maturity.

3
Show ownership, not participation

Replace "helped with" and "contributed to" with "architected," "led," or "owned." Hiring managers want individual contributors who drive outcomes, not people who attend meetings.

4
Keep it to one page

Unless you have 15+ years of experience, a single page forces you to prioritize. Every line should demonstrate a skill the target role requires.

Related Guides

→ Software Engineer Resume Examples: Junior, Mid, and Senior → Resume Keywords for Software Engineers → How to Quantify Your Resume Bullet Points → The Complete ATS-Friendly Resume Guide → How ATS Scoring Actually Works

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