Deep learning engineer with 7 years of experience training and optimizing neural networks for speech recognition at scale. Led a conformer model rewrite that cut word error rate by 21% on noisy call audio. Brings distributed training on multi-node GPU clusters, mixed-precision optimization and careful experiment design.
Experience
Deep Learning Engineer, Vocalis Labs (Pittsburgh, PA)
Jan 2022 – Present
Trained a 600M-parameter conformer ASR model on 80,000 hours of audio, cutting word error rate 21% on contact-center calls
Scaled training to 64 A100 GPUs with PyTorch FSDP and bf16, reducing time per experiment from 9 days to 3
Distilled the model to 120M parameters with INT8 quantization, meeting a 150 ms streaming latency target
Built an evaluation harness covering 14 accents and noise conditions, now required for every model release
A strong Deep Learning Engineer resume shows the systems you built, the scale they run at, and the measurable effect on speed, reliability or cost. Put your most relevant recent role first and make every bullet show a result, not a duty.
Sections, in order
Summary
Experience
Projects
Skills
Education
Length: One page for most candidates; move to two only if every line is relevant.
Summary: Write two or three lines: your role, your strongest skills, and one result that proves them.
Skills to feature
Programming languages
System design
Cloud platforms (AWS, GCP, Azure)
CI/CD
Testing and code review
Databases and SQL
Observability
Security fundamentals
APIs and integrations
Technical documentation
Example bullet points
Rebuilt the checkout service in Go, cutting p95 latency from 900 ms to 180 ms for 2M monthly users
Introduced contract tests and staged rollouts, reducing production incidents by 45% in two quarters
Migrated 30 services to Kubernetes with Terraform, lowering cloud spend by $250k a year
Tips from recruiters
Group skills by category (for example "Languages: Go, TypeScript") so recruiters and ATS parsers find them quickly.
Link to a repository, demo or write-up for at least one project you can talk about in depth.
Name the stack inside each bullet; keyword matching happens at the bullet level, not only in the skills list.
Recommended templates
Classic (ATS-friendly): Jake's-style LaTeX classic: small caps and hairline rules
Tech (ATS-friendly): Developer style: mono headings and tags, sans body
Modern (ATS-friendly): Clean single column with accent headings and rules
Deep Learning Engineer resume FAQ
What should a Deep Learning Engineer resume include?
A Deep Learning Engineer resume should show the systems you built, the scale they run at, and the measurable effect on speed, reliability or cost. Use these sections in this order: Summary, Experience, Projects, Skills and Education. Put your most relevant recent role first and make every bullet show a result, not a duty.
What is the best resume template for a Deep Learning Engineer?
Classic, Tech and Modern work well for Deep Learning Engineer roles. They are single-column with standard headings, so applicant tracking systems (ATS) parse them reliably.
How long should a Deep Learning Engineer resume be?
One page for most candidates; move to two only if every line is relevant.
Which skills should a Deep Learning Engineer put on a resume?
Prioritize the skills named in the job posting. Common ones for Deep Learning Engineer roles are Programming languages, System design, Cloud platforms (AWS, GCP, Azure), CI/CD, Testing and code review and Databases and SQL.
How do I write strong Deep Learning Engineer resume bullet points?
Start with an action verb, say what you did, and end with a measurable result such as latency, uptime and deploy frequency. For example: "Rebuilt the checkout service in Go, cutting p95 latency from 900 ms to 180 ms for 2M monthly users."