Machine Learning Engineer Job Description

About the role

Our team needs a Machine Learning Engineer to bridge the gap between promising models and production systems that serve real users. You will build training and serving pipelines, optimize models for latency and cost, and keep everything monitored and reproducible. This role suits an engineer who thinks in both model quality and production reliability.

Key responsibilities

• Build and maintain production ML training and serving pipelines
• Deploy, optimize, and monitor models in production
• Collaborate with data scientists to productionize models

Responsibilities

• Build reproducible training pipelines with versioned data, code, and model artifacts
• Deploy models as low-latency, scalable services or batch jobs in production
• Optimize models and inference for latency, throughput, and cost, including quantization or distillation
• Set up monitoring for model drift, data quality, and prediction performance with alerting
• Design and maintain feature stores and pipelines that serve training and inference consistently
• Automate retraining, evaluation, and rollout with CI/CD for models
• Partner with data scientists to harden prototype notebooks into production code
• Build A/B and shadow deployment frameworks to test new models safely
• Containerize and orchestrate ML workloads with Docker and Kubernetes on the cloud
• Debug production incidents involving latency, accuracy regressions, or pipeline failures

Requirements

• Bachelor's or Master's degree in computer science, engineering, or a related technical field
• 3 or more years in software engineering with a focus on machine learning systems
• Strong Python skills plus solid software engineering fundamentals and testing habits
• Hands-on experience deploying and operating ML models in production
• Working knowledge of containers, cloud infrastructure, and CI/CD