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Build an AI R&D team around the work it must own—not a standard list of job titles. First define the problem, users, operating context, and risks; then assign ownership for research, data, engineering, evaluation, deployment, monitoring, and governance. The right mix depends on whether you are pursuing fundamental research, applied research, or a product, and on what is currently preventing progress. There is no evidence-backed universal team size or hiring order.
Start with the kind of AI work you are doing
A team investigating new methods has different needs from one adapting an existing model or integrating a third-party service into a product. Before writing job descriptions, establish the system’s purpose and boundaries: what problem it addresses, who will use or be affected by it, what outcome counts as success, what data and deployment environment are involved, and what could go wrong.
NIST’s AI Risk Management Framework (AI RMF) treats context, objectives, assumptions, requirements, data, and system boundaries as part of design work. Its functions are voluntary and can be tailored to an organization’s context, resources, and capabilities; they are not a prescribed org chart or legal mandate. See the NIST AI RMF Core and NIST’s AI actor task descriptions.
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| Type of effort | What the team is trying to do | Staffing emphasis |
|---|---|---|
| Fundamental research | Investigate scientific questions or develop new methods. | Research expertise, experimental design, research engineering, and enough data and compute capability to make findings reproducible. |
| Applied research | Adapt or assess methods for a defined problem, domain, or set of constraints. | Research and ML engineering paired with data expertise, domain knowledge, and rigorous evaluation. |
| Product development or model integration | Deliver a usable system, often by adapting existing models or services. | Software and ML engineering, data integration, product and human-factors expertise, operations, and evaluation suited to the intended use. |
These are planning distinctions, not rigid categories. An effort can move between them, and the same team may do more than one kind of work.
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Assign lifecycle responsibilities before choosing titles
AI work does not end when a model has been trained or a prototype has performed well. NIST’s actor taxonomy covers design and data, model development, integration and deployment, operation and monitoring, testing and evaluation, human factors, impact assessment, and governance. It includes contributors such as data scientists, developers, domain and socio-cultural experts, accessibility specialists, product managers, evaluators, operators, privacy and legal experts, and affected-community representatives.
Translate that breadth into named ownership. A small team may combine several responsibilities in one role or bring in shared expertise; the important point is that work does not go unowned. For each area, decide who does the work, who reviews important decisions, and who can raise or resolve concerns.
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- Research question and method: Who frames the hypothesis, selects an approach, and interprets what the evidence supports?
- Data: Who establishes data suitability, quality, provenance, access, and the limits of what the data can represent?
- Model and software: Who builds or adapts the model and integrates it into the intended system?
- Evaluation: Who defines success measures, tests failure cases, and documents uncertainty and limitations?
- Deployment and operations: Who handles release, observability, maintenance, incidents, and ongoing assessment?
- People and risk: Who understands the affected users and domain, and who addresses relevant human-factors, security, privacy, legal, and governance concerns?
Match roles to the work that needs doing
The titles below are common ways to organize responsibilities, not a mandatory hiring slate. Some are combined in small teams; in higher-risk or more complex work, distinct ownership and review can be valuable.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors| Role or function | Typical contribution | Skills and evidence to look for | When it matters most |
|---|---|---|---|
| Research scientist or applied scientist | Frames questions, chooses methods, designs experiments, and interprets findings. | Experimental design, statistics, mathematical and domain reasoning, literature fluency, and clear technical writing. | Original research, deep adaptation, or work where sound conclusions depend on more than integrating an existing model. |
| Research engineer | Turns research ideas into reproducible experiments and scalable implementations. | Strong programming, data and model pipelines, experiment tracking, debugging, and systems awareness. | Research results are difficult to reproduce, compare, or move into a working implementation. |
| Machine learning engineer | Builds, adapts, deploys, and maintains models and inference services. | Software engineering, ML fundamentals, deployment and reliability, and measurement of performance and cost. | The organization owns production integration or model serving. |
| Data engineer or data scientist | Builds and validates data flows, explores datasets, and measures outcomes. | Data modeling, quality and provenance, statistics, analytical programming, and visualization. | Data suitability, curation, or outcome measurement is central to the work. |
| Evaluation, safety, or red-team specialist | Defines evaluations, probes failure modes, assesses trustworthiness, and follows incidents. | Measurement, benchmark design, adversarial testing, uncertainty analysis, risk assessment, and documentation. | Performance claims need credible evidence or failure could have significant consequences. |
| Domain expert or subject-matter researcher | Checks whether the system fits real tasks, domain norms, and operating conditions. | Deep knowledge of practice, users, and the consequences of mistakes. | The system is intended for a specialized or high-impact domain. Involve this expertise before key design decisions are fixed. |
| Product manager, UX, or human-factors expert | Connects technical work to user needs, workflow, oversight, and usability. | User research, requirements, communication, and human-centered design. | The system changes how people make decisions or complete work. |
| Security, privacy, legal, policy, or governance expertise | Identifies relevant constraints, rights, misuse, data and third-party risks, and controls. | Applicable legal and regulatory knowledge, privacy and security practice, and risk management. | Bring in the expertise relevant to the system’s data, users, deployment, and risks; it may be internal or shared, but access and ownership should be clear. |
| Platform, MLOps, or operations | Keeps training and deployed systems observable, reproducible, and maintainable. | Infrastructure, automation, reliability, monitoring, and incident handling. | Operational needs emerge, especially when systems must be monitored and maintained after release. |
NIST describes development actors as responsible for model building and interpretation, including creating, selecting, calibrating, training, or testing models and algorithms. Its broader lifecycle view is a useful reminder that development is one part of the work, not the whole team.
Make evaluation continuous and credible
Evaluation should shape the research question and continue through development and operation. Decide what the system is expected to do, what evidence would support that claim, which failure cases matter, and how performance will be checked after deployment. A single benchmark score or successful demonstration cannot, by itself, answer all of those questions.
NIST treats test, evaluation, verification, and validation (TEVV) as lifecycle work, including model validation and ongoing assessment in operation. It also says evaluators ideally are distinct from those performing test and evaluation actions. Where staffing permits, use independent review; where it does not, make review responsibilities explicit and give evaluators room to report problems without pressure to confirm the builders’ expectations. See the AI actor task descriptions and AI RMF Core.
Decide what to hire first by finding the bottleneck
There is no source-backed universal hiring sequence. Use the project’s current constraint to choose the next capability, and revisit the choice as evidence accumulates.
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- Map ownership. Assign responsibility for the research question, data quality, model work, integration, evaluation, deployment, monitoring, human oversight, and risk decisions. A person can own several areas initially; an unowned area is a gap.
- Identify the work that is blocked. If the team cannot form or answer meaningful research questions, add research strength. If results do not reproduce or ship, examine research engineering, data, or ML systems. If claims lack convincing evidence, strengthen evaluation. If users or the deployment context are unclear, involve domain and product or human-factors expertise.
- Set cross-functional minimums. Across the team, ensure access to experimental design and statistics, software engineering, sound data practices, domain knowledge, evaluation, and relevant security, privacy, legal, and risk expertise. NIST’s AI RMF Playbook recommends interdisciplinary competencies and hiring practices at the outset.
- Reassess after early evidence. Early work may reveal that the limiting factor is data, compute, evaluation, integration, domain access, or governance. Add specialist capacity as the work and its risks require it rather than applying a generic headcount ratio.
Screen for evidence, not just titles
Build a consistent scorecard around the actual responsibilities. NIST’s lifecycle roles and an illustrative Stanford GUIDE-AI data scientist vacancy point to a mix of technical, measurement, domain, and communication skills; neither defines a universal job specification.
- Research depth: Can the candidate frame a tractable question and distinguish evidence from intuition?
- Engineering quality: Can they produce reliable, reproducible work and integrate it into the intended environment?
- Measurement rigor: Can they choose suitable metrics, reason about uncertainty, and investigate failure cases?
- Data and domain competence: Can they recognize unsuitable data, context mismatch, or invalid assumptions?
- Operational readiness: Can they monitor, maintain, or respond to issues in the system they help deploy?
- Risk and governance coverage: Can they address the safety, security, privacy, legal, accessibility, or impact concerns relevant to their work?
- Collaboration and communication: Can they explain assumptions, results, limitations, and risks to colleagues and decision-makers?
Use role-relevant work samples and ask candidates to explain a research or engineering decision: its assumptions, how success was measured, what failed, and how they communicated uncertainty. Evaluate candidates consistently against the same responsibilities. This is a practical hiring approach, not an interview method prescribed by NIST.
For an example rather than a template, Stanford’s GUIDE-AI Data Scientist posting lists statistics, evaluation, fairness and bias assessment, data visualization, application development, and communication among role-specific skills. It is one institution’s vacancy, not a labor-market survey or a universal specification: Stanford GUIDE-AI Data Scientist posting.
Keep organizational accountability visible
Delegating technical work does not transfer organizational accountability for AI risk. NIST’s AI RMF assigns executive leadership responsibility for decisions about AI risks and calls for documented roles, training, engagement, and attention to third-party risks. Make clear who can approve consequential decisions, who is responsible for controls and escalation, and how external model or service providers affect the system’s risk picture. The NIST AI RMF Core is voluntary guidance, not a substitute for applicable legal advice or obligations.
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Stanford HAI’s 2026 AI Index reports that industry produced over 90% of notable frontier models in 2025. That figure describes frontier-model production, not the staffing needs of a typical organization building an applied system or integrating a model. It does not establish a recommended team size, role mix, or hiring priority for an individual company. See the Stanford HAI 2026 AI Index.
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