Machine Learning Engineer
Takes machine learning models from prototype to reliable, scalable production systems.
Builds statistical and machine learning models to uncover patterns in data and predict future outcomes.
Also known as: ML Scientist, Applied Scientist
Compare with another careerTurns business questions into testable hypotheses, explores and cleans large datasets, and builds predictive models that are validated with statistical rigor. Communicates findings and model limitations honestly to product and business teams, and often works alongside data engineers to move models toward production.
Primarily desk-based work with data platforms and notebooks, embedded within a product or analytics team, with periodic presentations to leadership.
Typical entry point
Junior or associate data scientist role in a tech company, analytics team, or research group
Core
Also relevant
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.
Turns raw business data into clear reports and dashboards that help teams make decisions.
Designs and maintains the pipelines and infrastructure that move and store data reliably at scale.
Builds applications and systems powered by AI models, integrating them into products people actually use.
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.