Machine learning engineer

Builds and runs machine learning and AI features, from model training to LLM apps.

351 skill packs · 151 MCP servers · 14 plugins

Under Software engineer. Also under it: Frontend engineer, Backend engineer, Full stack engineer, Mobile engineer, Data engineer, Solutions architect, Game developer

Role

Product manager Technical product manager Product owner Product operations Program manager Project manager Scrum master or agile coach Software engineer Frontend engineer Backend engineer Full stack engineer Mobile engineer DevOps or site reliability engineer Platform engineer QA or test engineer Security engineer Machine learning engineer Data engineer Engineering manager Solutions architect Game developer Embedded or hardware engineer Data analyst Business analyst Data scientist Analytics engineer BI developer Product analyst Marketing analyst Product designer UX researcher UI or visual designer Graphic designer Content designer or UX writer Brand designer Marketing manager Growth marketer Product marketing manager Content marketer SEO specialist Performance marketer Social media manager Community manager Copywriter Account executive Sales development representative Sales engineer Account manager Customer success manager Customer support agent Partnerships and business development Operations manager Business operations or strategy Revenue operations Supply chain and logistics Procurement Executive assistant Financial analyst Accountant Finance manager or controller Investment analyst Risk analyst Recruiter HR business partner People operations Learning and development Lawyer or in-house counsel Paralegal Compliance officer Privacy officer Founder Executive or general manager CTO or VP of engineering Chief of staff Management consultant Writer or journalist Editor Technical writer Video producer or creator Academic researcher Teacher or educator Student Clinician Healthcare administrator

Industry

All industries SaaS and B2B software Consumer apps E-commerce and retail Fintech and payments Healthcare Marketplaces and platforms Games and live ops Banking and insurance Media and publishing Education Government and public sector Manufacturing and industrial Logistics and transportation Real estate and construction Travel and hospitality Telecommunications Energy and utilities Automotive Biotech and pharma Agencies and consulting Nonprofit Legal services AI and developer tools Crypto and web3

Workflows for this work

The job in 5 areas. Each part is a workflow: open it for its steps and the tools that do each one. Built from occupational data and the field's own body of knowledge; sources at the end.

Machine learning engineer198 skill packs · 87 MCP servers · 7 pluginsEach part is a workflow. The number beside it is how many tools help with it

1Frame the problem

Decide what the model should do, how to judge it and what could go wrong.

2Prepare the data

Collect, label, clean and split data, and build features the same way everywhere.

3Train and evaluate

Train models or adapt language models, then test them honestly.

4Ship and serve

Put the model behind a service and automate how it is rebuilt.

5Monitor and improve

Watch the model in use and explain its results and limits.

Sources for this tree, 6

VendorDefine success criteria and build evaluationsAnthropic
VendorMLOps: Continuous delivery and automation pipelines in machine learningGoogle Cloud
StandardAI Risk Management FrameworkNIST
Occupational dataComputer and Information Research Scientists (15-1221.00)O*NET OnLine, U.S. Department of Labor
Occupational dataData Scientists (15-2051.00), summary report: tasks and detailed work activitiesO*NET OnLine, U.S. Department of Labor
PractitionerRules of Machine Learning: Best Practices for ML EngineeringMartin Zinkevich, Google for Developers

O*NET occupations consulted: 15-2051.00, 15-1221.00. Read on 2026-10-04.

Everything suggested for this role

Each pick says which of the role's tools or work it matched. Auto-listed means automated checks only.