Machine Learning Engineer Resume Example & Template (2026)
ML hiring managers want to see models you actually shipped to production — not just notebooks. Lead with the model class, the framework, the deployment surface (real-time / batch / edge), and the business metric you moved. 'Built a recommender' is invisible; 'lifted CTR 11.4% via two-tower retrieval served at 8k QPS' is hired.
Machine Learning Engineer resume example
Marcus Chen
Senior ML Engineer · Ranking & Personalization · 6 yrs
ML engineer with 6 years productionizing ranking and recommendation models. PyTorch, Ray, Triton, Kubeflow. Shipped models serving 40M MAU.
- Re-architected homepage ranker as a two-tower model with learned embeddings; lifted session CTR 11.4% and revenue per session 6.8% (A/B, n=22M).
- Built feature store on Feast + Redis serving 380 features at p99 < 8ms; cut training/serving skew incidents from 14/quarter → 1/quarter.
- Owned MLOps pipeline (Kubeflow + Argo + MLflow) covering 9 models with automated retraining, shadow deployment, and rollback in < 4 minutes.
- Cut GPU inference cost 41% by distilling a 1.3B-param ranker into a 180M-param student model with <0.6 pt offline AUC loss.
ATS tips for machine learning engineer resumes
Top skills for machine learning engineer resumes
Hard skills
Soft skills
Best templates for machine learning engineers
Common machine learning engineer resume mistakes
- Listing Kaggle competitions in place of production work past your first job.
- Citing model metrics (AUC, F1) with no business metric attached.
- Writing 'used machine learning to' — name the model class and the framework.
- Putting publications above experience for an industry role — flip the order unless you're applying to a research lab.
Machine Learning Engineer salary insights
Entry-level
$135k – $170k
Mid-level
$185k – $260k
Senior
$285k – $480k+ (Staff / Principal / Research Eng)
U.S. base + bonus + equity, 2025 Levels.fyi + Glassdoor.
Frequently asked questions
Should I list every Kaggle competition?
Only if you have under 2 years of industry experience. Past that, one or two top-tier finishes are fine; the rest is noise relative to shipped models.
How do I show LLM experience without overclaiming?
Be specific: which base model, which fine-tuning method (LoRA, QLoRA, RLHF, DPO), the eval suite you used, and the production deployment surface. Generic 'worked with LLMs' bullets get filtered.
Do I need a PhD to be a Senior ML Engineer?
No — but if you don't have one, your resume needs to lean harder on shipped models, A/B wins, and system-design depth to compensate for the credentialing gap.
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