New Roles · Engineering AI Builders

AI Research Scientist. The role, the market signal, and how to build it in your org.

Pushes the model frontier: novel architectures, evals, fine-tuning research. Distinct from the AI Engineer who ships; the researcher invents and proves what the builders then deploy.

In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.

189 postings · 135 distinct titles · from 264,613 real job postings · see the live data →

What the market calls it
AI Research ScientistApplied ResearcherML Research ScientistResearch Engineer (AI)AI Scientist
Hiring this role in our corpus right now
Capitalone 58Bosch 26Anthropic 10Scaleai 9Cisco 8

What the postings ask this role to do

2,633 tasks extracted from real AI Research Scientist job descriptions, classified Automate / Augment / Human-only. Only 1% can be fully automated: companies are hiring this role for the judgment, not the keystrokes.

Automate
  • Process and manage datasets.
  • Label data for model training.
  • Prepare data for model training and evaluation.
Augment
  • Build ai foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
  • Build large deep learning models for language, images, events, or graphs.
  • Build ai foundation models through design, training, evaluation, validation, and implementation phases.
  • Deliver libraries, platform-level code, or solution-level code to existing products.
  • Conduct applied research to advance the latest ai developments into customer experiences.
Human-only
  • Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver ai-powered products that change how customers interact with their money.
  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
  • Engage in high impact applied research to take the latest ai developments and push them into the next generation of customer experiences.
  • Develop talent within the team and beyond.
  • Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver ai-powered products.
How we build it in your org

From the market's version of this role to your version of it

1. Define workflow responsibilities
Start with the workflows this role supports: the outcomes, decisions, and handoffs it owns. Task Intelligence examines the tasks within that work and how people and agents can share responsibility.
2. Define your version
Your team composes the job description for your org's variant of the role, grounded in those responsibilities and the task evidence rather than a copied template.
3. Practise and assess readiness
Build on the domain and technical expertise your people already bring. Use relevant Simulations, GenAI Sandboxes, and Skill Validation Assessments to practise changed responsibilities and demonstrate capability. Revisit preparation when the work changes.

Start with the work, not the org chart.

Define what this role will own, then connect those responsibilities to practice and assessment.