Machine Learning Engineer

As a Machine Learning Engineer, you're responsible for taking machine learning models from prototype to production. You're not just training models in notebooks — you're building the infrastructure, pipelines, and deployment systems that make ML work reliably in real applications. This means writing production-quality code, optimizing model performance, managing training pipelines, and ensuring models behave correctly once they're serving live traffic.

Your day typically involves a mix of coding, experimentation, and debugging. You might spend the morning optimizing a model's inference latency, then work on a feature pipeline in the afternoon, then troubleshoot why a model's predictions drifted in production. You need strong software engineering fundamentals — version control, testing, CI/CD — combined with enough ML knowledge to understand what the models are doing and why they're failing.

The biggest challenge is the gap between research and production. What works in a Jupyter notebook often breaks in production — data distributions shift, latency requirements bite, edge cases multiply. You need to be pragmatic about tradeoffs between model complexity and operational reliability. The people who thrive here are strong engineers who are genuinely interested in ML but don't romanticize it.