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Applied ML, research eng, and MLOps all want different things. Your resume probably reads like all three.

Recruiters for applied-ML roles skim for production metrics. Research-eng roles skim for papers and scale. MLOps skims for infra. A resume that tries to speak to all three convinces none of them. We reshape your bullets to match the exact flavor of ML role in the job description — without overclaiming a single thing.

Real rewrites

Before the optimizer. After.

Applied ML Engineer · Recommendations platform
Original
•Built ML models for recommendations.
Optimized
Shipped two-tower retrieval model serving 90M req/day; drove +4.2% watch-time and reduced training cost 38% via mixed-precision on H100.
ML Research Engineer · Foundation-model lab
Original
•Did experiments on training pipelines.
Optimized
Authored distributed training stack for 7B→70B parameter sweeps; reduced wall-clock by 2.8× with sequence packing and ZeRO-3 tuning.
MLOps Engineer · Computer-vision startup
Original
•Managed the model deployment system.
Optimized
Built Kubernetes-native serving stack for 40+ vision models; drove inference cost from $0.011 → $0.003 per call via dynamic batching and TensorRT compilation.
Optimize my ML resume
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→Only your real experience — we never invent metrics, skills, or achievements
→Every change explained — you understand exactly what changed and why
→ATS-safe output — single column, standard headings, no tables or graphics
→Export to PDF, DOCX, or plain text — send it as-is or paste into any template
Ready to send a better resume?
One optimization. One job description. $5. If it doesn't change how your resume reads, you haven't lost much.
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