Quality Systems Lead at Encord in London

Job description

About us We're solving AI's hardest problem: alignment. Encord is the data layer for Physical AI and enterprise, serving as core infrastructure for 300+ of the world's leading AI teams, including Woven by Toyota, Hitachi, Skydio, AbbVie and Dyna, Walden and Agility Robotics. From autonomous vehicles and robotics to sports and healthcare, we're solving AI's hardest problem: turning complex, real-world data into production-ready intelligent systems. We're a team of founders, scientists, quants, researchers, and former Olympians, who’ve helped build some of the most successful companies in Silicon Valley, and alumni of MIT, Stanford, UC Berkeley, Oxford, Cambridge, and Imperial. We’ve raised $110M from legendary technology investors, early backers of companies like Databricks, ElevenLabs and Vercel, and we’re growing at an extraordinary pace. We're looking for exceptional people who want to help build a category-defining company, operating with high urgency and taking real ownership to help shape the future of AI. The role We're hiring a Quality Systems Lead to own how Encord measures the quality of the human data we deliver to frontier AI labs, physical AI companies and enterprise AI teams — the standard itself, the systems that evaluate against it, and the audit function that produces the ground truth behind both. Data quality is what our customers buy. As we scale across data types — image and video, document, medical, LLM evaluation, robot teleoperation, egocentric capture — quality coverage cannot scale linearly with headcount. So this role has two halves that make each other work. You will build automated evaluation: model-assisted and LLM-based screening, agreement analysis at scale, anomaly and drift detection across annotation output. And you will build and run a dedicated audit team of around ten specialists in India, whose judgements become the labelled ground truth that trains and calibrates that automated layer. As coverage automates, the audit team moves up to the cases models can't judge and to generating gold sets for each new data type we take on. It is an unusual combination — engineering and consistent QC operations in one person — and it is the combination the job needs. You will also have an advantage your counterparts elsewhere in the industry don't: Encord owns the platform this work runs on, so the measurement you build can become native capability in the product rather than internal tooling. What you'll do • Build automated dataset quality evaluation and root-cause detection — model-assisted and LLM-as-judge screening, agreement analysis at scale, anomaly and drift detection across annotation output • Hire, train, calibrate and manage a dedicated audit team of around ten specialists based in our India operation, held to inter-rater agreement and catch rate rather than volume audited • Turn audit output into labelled ground truth that trains and validates the automated layer, and manage the ratio of automated to manual coverage deliberately over time • Own the quality standard for every data type we deliver — written rubrics with worked edge cases, golden sets, and acceptance criteria agreed with the customer, alongside the Special Projects lead, before the first batch ships • Build scoring systems that rank annotator and reviewer performance and feed routing, staffing and offboarding decisions • Set the pass thresholds that certification gates on, so nobody works a project queue without having demonstrated they meet the standard • Report quality KPIs to leadership, and into the reporting our Special Projects leads take to customers: accuracy against client spec, inter-annotator agreement, rework rate, cost of rework, and coverage • Work with Project Management on remediation — you produce the measurement and the diagnosis, production owns fixing the project, and the standard stays independent of the people being measured • Own the unit economics of quality: cost per audited unit, and the coverage you buy