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Encord

AI-native data infrastructure for Physical AI

Senior Product Engineer

Full-Stack EngineeringUpdated on Nov 13
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Strong full-stack engineering experience and a track record of shipping products.
  • Deep fluency in at least one high-level programming language; Python and TypeScript are used by the team, but depth matters more than a specific language.
  • Strong front-end skills and comfort building polished, responsive interfaces; React is preferred.
  • Experience building at scale in a fast-paced startup or high-growth environment.

Responsibilities

  • Build scalable back-end systems and intuitive front-end interfaces, owning features from database schema through rendered user interface.
  • Develop tooling for machine-learning workflows, including model evaluation, active learning, and automated quality pipelines.
  • Iterate rapidly on the product and ship high-quality features while maintaining strong continuous integration/continuous delivery and testing practices.
  • Work closely with customers, product, and design to understand problems and deliver solutions.
  • Contribute to architecture decisions, engineering culture, and product direction.

Desired Qualifications

  • Familiarity with cloud infrastructure, preferably Google Cloud Platform, and containerized deployment with Kubernetes.
  • Exposure to machine-learning workflows or machine-learning infrastructure.

About the Company

Encord

AI-native data infrastructure for Physical AI

Encord provides an AI-native data infrastructure platform for Physical AI, helping enterprises manage multimodal data for robotics, autonomous systems, and related applications. It offers Encord Annotate for collaborative multimodal annotation, Encord Active for data curation and quality evaluation, and Encord Index for petabyte-scale dataset search and management, covering the full data lifecycle from collection to deployment. The platform acts as a universal data layer for production-grade Physical AI, addressing the infrastructure gap left by cloud-era tools focused on text and tabular data and emphasizing multimodal data and embodied AI. Its goal is to enable teams to build, evaluate, and deploy real-world AI systems more efficiently with scalable, reliable data tooling.

Company size
201-500
Company stage
Series C
Total funding
$107.1M
Headquarters
London, United Kingdom
Founded
2020

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