Human Data Program Manager

Encord · Londonmid

En corto

▸Gestiona proyectos clave de datos humanos para AI avanzada, desde definir workflows hasta supervisar equipos remotos.
▸Eres el experto final en estándares de anotación, resolviendo casos complejos y manteniendo calidad y velocidad.
▸El rol es crítico: lideras proyectos de alto riesgo en dominios como robótica e imagen médica, donde el error es costoso.

Strong written and verbal communication in English

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¿Qué piden?

✓Capacidad para traducir requisitos ML en flujos de anotación claros.
✓Experiencia manejando equipos de anotación a distancia con métricas de calidad y productividad.
✓Enfoque orientado a resultados con instinto para actuar rápido, no solo planificar.
✓Habilidad analítica para monitorear throughput, calidad y utilización en tiempo real.
✓Experiencia en roles con múltiples proyectos, prioridades cambiantes y tiempos muertos.
✓Comunicación clara y persuasiva, especialmente con equipos remotos y multidisciplinares.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Human DataAnnotation WorkflowsQuality SystemsML RequirementsData CurationProject DeliveryPerformance MetricsTask AllocationFeedback LoopsCross-functional Collaboration

¿A quién escribirle en Encord?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

About us Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production. Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator. The role We're hiring Human Data Program Managers in London to run Encord's highest-stakes human data projects end to end — the work for frontier AI labs and physical AI companies, in specialist domains such as medical imaging and robotics, and the data types we are running for the first time. These are the projects where the technical complexity is highest and where a mistake is expensive. You will work at the point where machine learning requirements become annotation work: translating what a research team actually needs into workflows, standards and instructions that a specialist workforce can execute at volume, then holding the result to a measurable standard. This is the load-bearing role in the business, and it is a hands-on one. You will know your projects' annotation standards better than anyone at the company, teach them to the annotators producing the work, decide who works on what, judge who is performing and who should come off, resolve the edge cases nobody anticipated, and answer for the delivery timeline. You will run several projects at once, and you will own how they are run. What you'll do Own delivery of your projects end to end — throughput, quality and timeline Translate complex machine learning requirements into clear annotation workflows, and design the process that produces the data the model actually needs Become the deepest expert at Encord on your project's annotation standards, and the person who resolves ambiguity and edge cases as they surface Maintain quality through process refinement, auditing and structured feedback loops, rather than through inspection at the end Train annotators onto the project and keep them improving — building the material yourself until our Learning & Development Specialist is in place Coach your annotation teams: give them the context behind the task, not only the rules, and develop their skills as the work gets harder Measure annotator performance and make the calls it implies: who continues, who needs retraining, who comes off the project Allocate tasks and manage the queue so that throughput and quality targets are met together rather than traded against each other Instrument the project at launch with the Quality Systems Lead — acceptance criteria, sampling plan, reviewer ratio — so it is measurable before the first batch ships Produce the delivery reporting for customers, and surface risk early enough that something can still be done about it Partner with Product and Engineering on process and tooling improvements, and feed recurring problems back into the playbooks, into training and into the platform Who we're looking for You go deep on the detail. Your instinct is to read the whole specification and annotate fifty items yourself before you assign anyone else Execution-oriented: you are measured by what shipped, not by what was planned, and you would rather fix a workflow than document one Analytically rigorous. You work in numbers — throughput, quality rate, utilisation — and you notice when one is being bought at the expense of another Technically fluent enough to work with ML teams on what they need and why, and to pull your own data rather than wait for it You make people calls on evidence rather than on impression, and you can have the difficult version of that conversation Organised under real load: several projects, several time zones, and requirements that move A clear writer and a strong cross-functional communicator.

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