Machine Learning Engineer
Takes machine learning models from prototype to reliable, scalable production systems.
Builds applications and systems powered by AI models, integrating them into products people actually use.
Also known as: Applied AI Engineer, GenAI Engineer
Compare with another careerIntegrates pre-trained or custom AI models, such as language or vision models, into software products, and designs the prompts, retrieval systems, and application logic around them. Focuses more on application-layer engineering than on training models from scratch, though may fine-tune existing models for specific tasks, and works with APIs, safeguards, and evaluation tools to keep AI features reliable.
Desk-based software development work, usually in a product engineering team, collaborating with product managers and designers as well as ML specialists.
Typical entry point
Software engineer role focused on AI features, or a junior AI engineer role in a product team
Core
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Core
Also relevant
These are useful, relevant skills to build over time — not a checklist you need to already have.
These are common routes in, not the only path — and not a university or course recommendation.
General tendencies for this career, not a guarantee — actual roles vary by employer and location.
Takes machine learning models from prototype to reliable, scalable production systems.
Designs and maintains the pipelines and infrastructure that move and store data reliably at scale.
Builds mathematical models used to price financial instruments, manage risk, and inform trading strategies.
Builds and maintains dashboards and reporting systems that give business leaders an ongoing view of performance.
Builds statistical and machine learning models to uncover patterns in data and predict future outcomes.
Turns raw business data into clear reports and dashboards that help teams make decisions.