ML Engineer

ML engineers bridge the gap between data science research and production systems. They typically start with a software engineering foundation and develop expertise in machine learning operations (MLOps). Junior ML engineers learn to deploy models, build feature stores, and create training pipelines.

Mid-level ML engineers design end-to-end ML systems including feature engineering, model training, evaluation, serving, and monitoring. They implement model versioning, A/B testing frameworks, and automated retraining pipelines. They optimize model inference for latency and cost.

Senior ML engineers architect the ML platform for their organization, making decisions about model serving infrastructure, feature platforms, experiment tracking systems, and GPU cluster management. They lead complex projects that bring research models to production at scale.

Staff-level ML engineers define the ML infrastructure strategy, build platforms used across the organization, and drive best practices for responsible AI deployment. They evaluate emerging technologies like large language models and determine how to integrate them into production systems.

The ML engineer role is among the most sought-after and highest-paid in technology, reflecting the specialized skill set required. Career progression leads to ML platform leadership, applied research, or broader engineering leadership roles.