Human Data Program Manager

Join Encord as a Human Data Program Manager in London, leading high-stakes AI data projects with a hands-on approach.

• 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
• 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

• 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

• 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