Recruit to the task.
Domain, language, schedule and cognitive requirements are defined around representative work.
Managed workforce
You define the AI program. We build the human operation.
For programs that need dozens or potentially hundreds of contributors, the problem is no longer recruiting alone. It is workforce design, calibration, scheduling, QA, continuity and reporting.
Create a repeatable system.
Team leads, queue management, escalation and performance visibility are designed around the program.
Calibrate quality continuously.
Review disagreements, edge cases and failure modes so the workforce learns with the task.
AI data, evaluation & human-feedback operations
A managed AI workforce should run with explicit tooling, QA, escalation and governance—not an anonymous task queue.
Human-in-the-loop work should be designed like an operation: clear taxonomy, rubrics, tooling, QA, escalation and measurable agreement—not an undifferentiated crowd.
Systems we can match for
Label StudioLabelboxSuperAnnotateCVATDoccanocustom evaluation portalsspreadsheets / QA dashboardssecure client tooling
Tell us the exact systems you use. We screen for relevant experience and learning ability rather than assuming every candidate knows every product.
Terms and workflows the role should understand
- taxonomy & ontology
- golden sets
- inter-annotator agreement
- consensus / adjudication
- sampling & QA
- preference ranking
- RLHF / human feedback
- benchmark / eval set
- rubric design
- PII / sensitive-data controls
How AI can support the work
- human evaluation of model outputs
- data labeling and validation
- red-team and adversarial review
- multilingual and domain-expert review with accountable QA
We design the workforce around the model risk, domain expertise, quality target and data-access rules—not only the number of annotators.
Hire around the real workflow
Send us the tools, workload and outcome you need someone to own.
Use the consultation brief for a structured requirement, or book a 15-minute discovery call now. We’ll match the role to your stack, operating context and accountability level.
Need 20, 50, 100 or more people?
Start with the workload, expertise, quality bar, security requirements and timeline—not a generic headcount request.