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 work with the biggest names in robotics and autonomous vehicles, and join a small, highly collaborative team at a crucial stage of accelerated development. 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.
What you'll do
- Build and maintain high-performance frontend experiences, including rendering complex 3D scenes with thousands of objects at smooth frame rates
- Work across the full stack — from backend data pipelines and storage systems to frontend visualisation and interaction
- Take end-to-end ownership of projects, from product and architectural decisions through to deployment, monitoring, and measuring user impact
- Work with visual, spatial, and sensor data to make sense of the physical world
- Tackle complex domains at massive scale using simple, well-crafted solutions
- Take initiative, be proactive in problem-solving, and continuously seek improvements
Who we're looking for
- 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
- At home with challenging frontend work: you're comfortable rendering complex 3D scenes, working with spatial and sensor data, and optimising for browser performance
- Impact-driven: you want your work to have a tangible outcome for people and aren't satisfied by products that don't see the light of day
- A builder: you enjoy all aspects of building a complete product, are comfortable moving across the stack, and love problem-solving from first principles
- Comfortable with uncertainty: happy to tackle problems without a predefined solution, and excited to make decisions autonomously as you go
- A team player: you contribute your best work and actively help others do the same — you enjoy levelling up those around you
Experience requirements
- 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
Tech stack
- Backend: Python
- Frontend: TypeScript and React
- Deployment: Kubernetes
- Infrastructure: GCP
- Machine learning: PyTorch, CUDA, Ray
We are technology agnostic at Encord and not looking for experience across all of these — as long as you're open to learning, please apply.
Why Encord
- 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
- Frequent travel opportunities across the U.S., London, and Europe
- Bi-annual company offsites, twice-weekly team lunches, and monthly socials
Top Skills
What We Do
Encord is the data layer for physical AI. We're how the world's most ambitious AI teams turn messy, multimodal data into production systems - from humanoid robots to autonomous vehicles to smart infrastructure. 300+ teams including Toyota, Skydio, and Maxar rely on Encord to curate, manage, and align the data their models actually need. $110M raised. San Francisco, New York, and London.
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