Founding Engineering Lead (Data Platform) | YC-Backed Physical AI & Robotics Startup |London (Remote) | Full-Time | Up to £150K + Equity

About the Company

Our client is a Y Combinator-backed physical AI data company building the foundational human video and motion datasets for leading robotics and embodied-AI teams. Focused on egocentric first-person video and UMI demonstration data, the company turns real-world capture operations into high-precision, versioned data products that foundation-model and VLA teams rely on to train physical robots.

Backed by a freshly closed seed round, their core constraint is supply, not demand. They are actively scaling deployment engineering, field operations, and capture hardware to keep up with massive customer demand.

The founders are building a three-person founding engineering team to set the technical standards, tooling, and evaluation methodology behind the world's reference dataset for robot learning.

About the Role

Reporting directly to the Founders, this is the foundational engineering search for the business. As the Founding Engineering Lead for the Data Platform, you will serve as the strategic and technical architect behind the end-to-end data platform, while guiding and integrating the specialist engineers (Robotics Perception and Robot Learning).

In the first six months, this is a hands-on platform role focused on building robust ingestion, job queues, review applications, and versioned customer delivery systems. You will take full ownership of technical execution - architecting infrastructure to handle 50+ TB per day of multi-modal uploads across intermittent global connections - and grow into technical management of the entire Egocentric & UMI Data Division.

Key Responsibilities

• Build resumable, recoverable upload and storage pipelines across global locations with unreliable connections, verifying file integrity and managing queued backlogs.

• Architect scalable job queues and worker services for video processing, motion estimation, and annotation, ensuring safe retries and clear error investigation.

• Build practical web interfaces for operators to browse recordings, play previews, select task intervals, edit labels, inspect quality flags, and approve data.

• Package approved recordings with metadata and derived outputs into reproducible, versioned exports that maintain full lineage back to raw source files.

• Set priorities with the founders, review code and architecture, optimize cloud storage/compute costs, and guide the growth and hiring of the engineering team.

Skills & Competencies

• Senior/Staff-level mastery in building and operating distributed data systems, job queues, cloud storage, and practical web interfaces.

• Deep experience with large-file uploads, local buffering, network recovery, duplicate handling, and cost management.

• Ability to set acceptance criteria, delegate, and arbitrate quality, speed, and cost trade-offs across perception and machine learning disciplines.

• High comfort balancing build-vs-buy decisions, making scope calls under ambiguity, and shipping production-grade code without dedicated frontend resources.
  
  

Qualifications & Experience

• 6–12+ years of experience as a Senior/Staff Engineer, Tech Lead, or hands-on Engineering Manager.

• Proven track record of personally building, shipping, and maintaining data-heavy production platforms (video, sensor data, ML platform, or cloud infrastructure preferred).

• Demonstrated experience leading engineering teams through delivery and product decisions.

• Active implementation depth - must have personally shipped and operated production code within the last 2–3 years.
  
  

Role Details

• Timing: Hire First (in seat first to help hire the specialist perception and robot-learning engineers).

• Location: London (remote).

• Package: Competitive base salary up to £150K + significant founding-engineer equity with total platform ownership.

• Career Pathway: Direct track to step into overall technical leadership of the Egocentric & UMI Data Division as the business scales.