Hiring nowAI research talent and programme operations

AI Research Programme Manager

Also called AI Research Programme Manager · Research Operations / Research PM (AI) · Technical Programme Manager, Research

An AI Research Programme Manager keeps research work moving across teams while protecting scientific validity. They make dependencies, risks and decisions visible, so leaders can back the right work and stop the wrong work early.

What they do

They turn a set of research ambitions into a plan the team can actually run. They sit in experiment reviews, learn what a result does and does not show, and track milestones against that. They manage compute requests, data access, evaluation dependencies and handovers between researchers and engineers. They write decision records so the reasons for a direction are not lost. They run planning cycles, flag slippage early, and tell leadership plainly when a delivery date and a scientific answer pull in different directions. They also help new researchers settle in and adopt shared ways of working.

What a strong hire delivers

In the first months, a strong hire gives the team a research programme plan that researchers recognise as accurate. Decision records exist and are used. Dependencies and risks are visible to leaders without chasing people. Planning meetings get shorter and more useful. Researchers spend less time on coordination, and leadership can see which work is progressing and which is stuck.

How you can tell they are good

Ask them to walk through a research programme they coordinated. Listen for how they separated a missed milestone from a negative result. Ask for a sample decision record or plan, with names removed. Strong people describe what they declined to push, explain technical trade-offs in plain terms, and can say what researchers thought of them.

Why it is hard to fill

The title varies from one lab to the next, so searches by title miss good people. Many programme managers have delivery skill but little ML literacy. Many researchers have the literacy but no interest in coordination. The overlap is small. Researchers can also be wary of anyone who looks like a process owner, so credibility matters as much as the CV.

Where to find them

Look in research operations teams at labs and larger AI groups, and among technical programme managers who have supported ML or data science teams. Others come from recruiting or workforce operations in research-heavy organisations, or from academic research management. Some are former researchers or engineers who moved sideways into coordination. Applied research groups inside larger technology firms are also worth a look.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Research Programme Manager

  1. Tell me about a research programme you coordinated. What did you track, and what did you leave to the researchers?

    A strong answer shows clear boundaries. They track dependencies, milestones and risks, and leave scientific judgement to researchers. Worry if they describe only ticket management, or if they claim to have decided what experiments were worth running.

  2. An experiment misses its milestone, but the result is informative. How do you report it?

    Look for a distinction between delivery slippage and scientific learning. They should explain what the result changes in the plan and record the decision. Worry if they treat every miss as a failure or hide it.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are an AI company looking for a Research Programme Manager to help our research teams plan, coordinate and make decisions clearly. You will work closely with researchers and engineers, keep the work visible to leadership, and protect the quality of our science while we deliver.

What you will do

  • Build and maintain the research programme plan with researchers and leads
  • Track milestones, dependencies and risks across teams
  • Write and maintain decision records so reasoning is easy to find
  • Coordinate compute, data access and evaluation needs between teams
  • Run planning cycles and reviews that researchers find useful
  • Report progress to leadership, separating delivery status from scientific findings
  • Support onboarding and shared ways of working for new research staff

What you will bring

  • Demonstrated experience coordinating technical or research work across several teams
  • Working understanding of how ML research is done, including experiments and evaluation
  • Clear written communication, shown through plans or decision records
  • Credibility with researchers, shown by examples of working alongside them
  • Judgement about when to escalate and when to resolve things directly
  • Experience introducing process that people actually adopted

Nice to have

  • Background in research operations or technical programme management
  • Experience with research planning and scheduling tools
  • Experience in recruiting or workforce operations for technical teams

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Research operationsAdvancedCoordinate research resources and reproducible execution
  • Technical communicationAdvancedExplain evidence and limitations to relevant readers
  • Change management and trainingWorkingSupport adoption through learning and organisational change
  • Worker and community participationWorkingInclude affected workers and communities in decisions

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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