Human Data Solutions Engineer

About the role

As a Human Data Operations & Solutions Engineer at Encord, you will sit at the intersection of technical sales and hands-on data operations. You are the expert who takes a prospect from first demo to a working proof of concept — not just by showing the platform, but by actually delivering a small-scale, high-quality annotation sample that demonstrates what best-in-class data operations looks like in practice. You'll own the full arc: leading technical discovery on demo calls, designing the annotation workflow, managing the delivery of sample datasets, and translating the results into a compelling case for the client.

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

Requirements

• 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