AI Researcher role guide
AI Researcher careers in the United States: responsibilities, stack (research, recherche, phd, scientist), training paths, salaries, and 7 active job listings on Ganloss.
AI Researcher is a core profile on the US artificial intelligence job market. Ganloss currently lists 7 open roles for this profile. This page covers day-to-day work, skills, typical career paths, and resources for applying in 2026.
Day-to-day responsibilities
Fundamental or applied research, publications, prototyping, and handoff to engineering for production. In industry labs or academia.
Stack & skills employers want
Math, deep learning theory, PyTorch/JAX, rigorous experimentation, scientific writing, arXiv monitoring.
Typical career path
PhD usually required. Post-doc or research scientist at corporate labs (Google, Meta, Anthropic, etc.) or deep-tech startups.
Cross-functional skills
Market keywords: research, recherche, phd, scientist, publication. Strong communication, experimentation discipline, product sense, and collaboration with business teams are expected on most AI Researcher roles.
Training & career switch
Common paths: CS/ML bachelor's or master's, accredited bootcamps, or self-taught portfolios with production projects. Browse LLM, ML, and MLOps training on Ganloss before you apply.
First steps to apply
1) Match your resume to Research keywords. 2) Document 2 production or POC projects. 3) Check salary guides for your target city. 4) Apply on Ganloss with your candidate profile.
Go further
FAQ
- Research scientist vs ML engineer?
- Research scientists explore and publish; ML engineers industrialize known solutions at scale. Research engineer roles blend both.
- Where to find AI Researcher jobs in the United States?
- Browse the AI Researcher job hub on Ganloss, filter by city or remote, and enable email alerts for new listings.
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