Human Data Program Manager

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.

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.

• 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

• 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. Most of the workforce delivering your project you will never meet in person

• Entrepreneurial: when a date is at risk your first move is to re-plan, not to escalate, and you invent the process where none exists

• Genuinely interested in AI and in what the data you are producing is actually for

• 3–7 years of professional experience, ideally combining operational delivery with analytical work — AI data or annotation operations, strategy consulting, or data and operations roles at leading technology companies

• Demonstrated end-to-end ownership of complex, multi-stakeholder workflows, with responsibility for the outcome rather than the coordination

• Working proficiency in Python or SQL

• Direct experience managing, coaching or performance-managing a distributed workforce

• Track record of holding quality and throughput at the same time, with the numbers to show it

• Experience translating a detailed technical specification into instructions other people can follow

• Bonus: direct experience of annotation, evaluation or model-training workflows

• Competitive salary, commission, and equity in a high-growth startup

• Strong in-person culture — most of the team works from our London office 4+ days/week

• 25 days annual leave + UK public holidays

• Annual learning & development budget

• Travel for customer visits, events, and conferences across the UK and Europe

• Company lunches twice a week

• Monthly socials & bi-annual team offsites

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