Encord

Encord

Software Engineer, Physical AI

San Francisco

Sponsorship not specifiedDetected 48 days ago
TypeScriptPythonReactFull-Stack DevelopmentGCPCloud PlatformsKubernetesMachine LearningPyTorchData EngineeringRoboticsProblem Solving

About the role

  • You'll solve problems of large-scale efficient data transfer and storage, complex domain modelling, applying the latest machine learning models, and squeezing performance out of web browsers.
  • You'll work with the biggest names in robotics and autonomous vehicles, and join a small, highly collaborative team at a crucial stage of accelerated development.

Responsibilities

  • Build and maintain high-performance frontend experiences, including rendering complex 3D scenes with thousands of objects at smooth frame rates
  • Experienced: you've seen a lot and built a lot - personally developed and maintained multiple systems from scratch, with a deep understanding of the trade-offs involved in building reliable, performant software at speed

Requirements

  • A team player: you contribute your best work and actively help others do the same - you enjoy levelling up those around you

Nice to have

  • Strong full-stack engineering experience, with a particular strength in complex, high-performance frontend work
  • Experience with 3D rendering, WebGL, or similar technologies for visualising spatial/sensor data is a strong plus
  • Proficiency in Python and/or TypeScript
  • experience with React a plus
  • Familiarity with cloud infrastructure (GCP preferred) and containerised deployment (Kubernetes)
  • Experience in robotics, autonomous vehicles, or Physical AI is a plus

Skills

  • Backend: Python
  • Frontend: TypeScript and React
  • Deployment: Kubernetes
  • Infrastructure: GCP
  • Machine learning: PyTorch, CUDA, Ray
  • Competitive salary, commission, and meaningful equity in a high-growth startup
  • Clear, accelerated growth opportunities as the company scales rapidly
  • Strong in-person culture: 4-5 days/week in our newly launched North Beach loft office
  • Flexible PTO to fully recharge
  • 18 paid vacation days in the U.S. plus federal holidays
  • Annual learning & development budget
  • Comprehensive health, dental, and vision coverage

Compensation

  • Competitive salary, commission, and meaningful equity in a high-growth startup

Company info

  • 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
  • We're building tools for the full Physical AI data stack - from data acquisition, through ingestion and curation at scale, to 3D rendering, annotation, and verification. You'll solve problems of large-scale efficient data transfer and storage, complex domain modelling, applying the latest machine learning models, and squeezing performance out of web browsers.
  • You'll be a key driver of progress on a core part of our product, operating with a high degree of autonomy and crafting performant, reliable, and maintainable solutions at speed.
  • 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.
  • We're building tools for the full Physical AI data stack - from data acquisition, through ingestion and curation at scale, to 3D rendering, annotation, and verification.

This listing is sourced directly from Encord's careers page and normalized into a canonical job model.