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, Next47 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
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
Why Encord
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
#J-18808-Ljbffr…