Learning & Development Specialist

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 a Learning & Development Specialist in London to design and deliver how Encord teaches the specialist workforce behind its human data — the annotators producing work for frontier AI labs and physical AI companies, in domains such as medical imaging and robotics, and on data types we are running for the first time. How well that workforce is taught is the largest controllable driver of the quality we ship.

You will work at the point where a technical specification becomes something a non-specialist can execute correctly: converting dense, sometimes ambiguous annotation guidelines into instruction a distributed workforce absorbs in days rather than weeks, and into assessment that proves they have.

That teaching problem is a real one, and it is why we are hiring for learning-science expertise rather than for training delivery. The people you teach are capable adults, but they are not domain experts, they come from a wide range of educational backgrounds, and they are frequently working in their second language. Managing cognitive load, breaking a specification into modules that build on each other, leading with worked examples before independent practice, and testing for what actually predicts performance on the job — that craft is the role.

It is a hands-on role. You will design the material and you will stand up and teach it, and it is measured in operational terms: ramp time to competence, first-pass quality, and how much of the workforce is qualified for the work we are selling.

What you'll do

• Turn a client specification and a set of annotation guidelines into teachable material within days of a project launch — modular, sequenced, and built on worked examples, edge-case libraries and annotated failure cases

• Design against explicit learning-science principles rather than instinct — manage intrinsic and extraneous cognitive load, chunk a dense specification into modules that build, scaffold then fade support, and use spaced retrieval so what is taught in week one survives to week four

• Run live training sessions yourself, remotely and to distributed cohorts, daily during a project ramp — and adjust in the room when a concept is not landing

• Build criterion-referenced assessments that decide readiness rather than test recall, with pass thresholds set alongside the Quality Systems Lead — so that nobody works a project queue without having demonstrated they meet the standard

• Design for a novice audience by default: no assumed prior knowledge, plain language, and material that works for someone reading in their second language

• Build durable curricula for the domains we sell repeatedly — medical imaging, document AI, LLM evaluation and red-teaming, robot teleoperation, egocentric capture — with progression into higher-skill, higher-rate work

• Coach the Human Data Program Managers into better teachers: session formats, feedback technique, and observation against a standard, since much of the instruction is delivered by them rather than by you

• Sit with annotators and watch where they get stuck, then redesign — most of what you need to know is visible in the first hour someone spends on a new task

• Partner with Product on what belongs inside the Encord platform as guidance at the point of work rather than in a course nobody re-opens

• Measure every programme against a before-and-after in quality and throughput terms, and retire the ones that do not move an operational number

• Grounded in learning theory, and able to say which principle you are applying and why — cognitive load, the worked-example effect, scaffolding and fading, spaced and retrieval practice, criterion-referenced assessment

• You teach as well as design. You will be in front of a cohort regularly and you are good at it, including when the room is not following you

• You design for the learner in front of you rather than for someone like yourself — assumed prior knowledge is the most common way training fails here

• Execution-oriented: a project launching next week needs material this week, and competence in days rather than a term

• Analytically rigorous. You read quality data and can tell which failures are teachable and which are not, and you are happy being measured on operational metrics rather than completion rates and satisfaction scores

• You work from a technical specification, and you are often the person who notices the guidelines contradict themselves

• A clear writer and a strong cross-functional communicator. Most of the workforce you are teaching you will never meet in person

• Entrepreneurial: this is the first dedicated learning hire here, and you will invent the system rather than inherit one

• A formal grounding in education, learning science or instructional design — a degree, postgraduate qualification, teaching qualification or equivalent professional training. This is a requirement for this role rather than a preference

• 3–6 years of professional experience designing and delivering training in an operational environment —where training was measured against production outcomes

• Demonstrable live facilitation to adult learners, remotely and at cohort scale, not only one-to-one coaching

• You have built curriculum and assessment from scratch, including assessments that genuinely discriminate between competent and not

• Experience teaching non-specialist adults, ideally across languages, geographies and levels of prior education

• Multi-format authoring: written documentation, video, and interactive assessment

• Comfortable working in data: enough fluency to evaluate your own programmes against quality and throughput reporting

• Bonus: a classroom or vocational teaching background, or a language-teaching qualification such as CELTA or equivalent

• Bonus: experience working with teams in India or comparable delivery geographies

• Bonus: familiarity with 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