encord - Human Data Solutions Engineer
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Requirements
• A sharp operator who combines structured, consulting-style thinking with hands-on execution — you're equally comfortable designing a workflow on a whiteboard and auditing annotation outputs in a spreadsheet • Technically fluent: you can query a database, write a Python script to automate a workflow, or dig into annotation outputs to identify quality issues — and you know enough about ML pipelines to speak credibly with engineers • A natural communicator who can run a compelling demo, walk through a POC delivery, and explain what it all means to a VP in plain language • Genuinely passionate about AI, with particular interest in robotics, autonomous driving, and the data operations challenges that come with physical AI • Entrepreneurial and collaborative — you take ownership, move fast, and thrive when the work is ambiguous and high-stakes • 1-3 years of professional experience, ideally spanning strategy consulting, AI/technology operations, or customer-facing technical roles (Solutions Engineering, Technical Account Management, or similar) • Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and client communication • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally involving human-in-the-loop or structured labelling workflows • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients • Hands-on experience with at least one major cloud platform (GCP, AWS, or Azure), including data storage and ML workflow patterns • Bonus: hands-on experience with computer vision, LiDAR, robotics sensor data, or autonomous driving datasets; prior exposure to data annotation platforms or quality management frameworks; experience in a customer-facing technical role at an AI company
Responsibilities
• Partner with Account Executives to lead the technical and operational strategy for complex enterprise sales cycles, co-owning the path to a successful proof of concept • Lead deep technical discovery sessions with ML Engineers, MLOps leaders, and non-technical stakeholders to understand data requirements and design the right annotation workflow • Manage end-to-end delivery of small-scale annotation POCs — translating complex AI requirements into clear instructions for annotation specialists, auditing outputs, and iterating on quality until the sample is client-ready • Build and deliver tailored demonstrations that combine platform capability with live, real-world annotation results — particularly for robotics, autonomous driving, and multimodal sensor data (LiDAR, camera fusion, etc.) • Act as a trusted advisor to clients on annotation workflow design, data quality, and the operational processes that underpin model performance • Provide structured feedback and guidance to annotation teams during POC delivery, ensuring outputs meet the quality bar required to win client confidence • Translate findings and operational results into clear value propositions for senior, non-technical stakeholders • Serve as the voice of the customer to Product and Engineering, channelling detailed technical feedback from enterprise clients to shape the roadmap
Benefits
• Competitive salary, commission, and equity in a high-growth startup • Strong in-person culture — most of the team works from our London office 3+ 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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