ML Engineer Roadmap
Train, fine-tune, and ship machine learning models to production. Math foundations, deep learning, and model serving for engineers building models, not just calling APIs.
$120K-$240K (US) / $60K-$140K (remote global)
18-24 months of focused learning
Steady demand concentrated in companies training proprietary models: recommendation systems, fraud detection, computer vision products, and any domain where a general-purpose LLM API cannot substitute for a purpose-built model.
›Market overview
ML Engineering is the traditional AI career path: building, training, and deploying models from data rather than consuming a foundation model API. This role requires genuine mathematical fluency and hands-on model training experience. It remains the higher-barrier-to-entry path compared to AI Engineering, but commands a premium for candidates who can actually debug a training run, not just prompt an existing model.
The 10,000-hour rule says mastery requires roughly that many hours of deliberate practice. At 1% improvement per day, you are 37x better in a year. This roadmap is a structured path, not a race — follow the steps in order, build the projects, and trust the process.