ML Engineer Resume Example

ML engineers productionize machine learning models, building the pipelines and infrastructure that take research from notebook to production. Your resume should prove you can train, deploy, monitor, and scale ML systems reliably. Show expertise across the full ML lifecycle, from data ingestion to model serving.

Key Skills

PyTorch / TensorFlowMLOps (MLflow, Kubeflow)Feature Stores & Data PipelinesModel Serving (TorchServe, TF Serving)Python & SQLDocker & KubernetesDistributed TrainingA/B Testing & Model MonitoringCloud ML Services (SageMaker, Vertex AI)Data Versioning (DVC)

Sample Resume Bullets

Deployed real-time recommendation model serving 10 M predictions/day with p99 latency under 50 ms on Kubernetes.
Built end-to-end ML pipeline (data ingestion → training → deployment) reducing model update cycle from 2 weeks to 4 hours.
Implemented model monitoring system that detected drift in production, preventing $800 K in potential revenue loss.
Optimized transformer model inference with quantization and batching, reducing GPU costs by 45% while maintaining accuracy within 0.3%.
Created feature store serving 200+ features to 8 ML models, eliminating training-serving skew across all production systems.

How to Write a ML Engineer Resume

Emphasize production deployment over research—show models you've shipped, not just trained.
Include infrastructure skills (Docker, K8s, cloud) alongside ML frameworks.
Quantify model performance AND business impact together.
Mention monitoring, drift detection, and model lifecycle management.
Show collaboration with data scientists and backend engineers.