Human Data Solutions Engineer

About us

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, Next
47 and Y Combinator.

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. With a strong focus on robotics and autonomous driving, you'll be working with some of the most technically complex and data-intensive AI use cases in the industry.

What you’ll do

• 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

Who we re looking for

• 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

Experience 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

• Dem…