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Encord

AI-native data infrastructure for Physical AI

Senior Software Engineer - Front-End Leaning

Frontend EngineeringUpdated on Nov 13
Full-timeLondon, UK

About the Job

Requirements

  • Strong frontend engineering experience, including production-grade work on complex, high-performance user interfaces.
  • Proficiency in TypeScript and React.
  • Experience building and maintaining multiple systems from scratch, with understanding of trade-offs involved in building reliable, performant software at speed.

Responsibilities

  • Build and own complex user interfaces and editor interfaces for visualizing and annotating multimodal data.
  • Render high-performance 3D/4D scenes with thousands of objects in the browser.
  • Work across the full stack when needed, including backend services, deployment, and styling.
  • Take end-to-end ownership of projects, from product and architectural decisions through deployment, monitoring, and measuring user impact.
  • Collaborate with a lean, high-trust team to tackle challenging problems.
  • Take initiative, proactively solve problems, and continuously seek improvements.
  • Develop and maintain reliable, performant, and maintainable solutions at speed.

Desired Qualifications

  • Experience with 3D rendering, WebGL, Canvas, or similar browser-based visualization technologies.
  • Familiarity with Python and backend development.
  • Experience with Kubernetes and GCP.

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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