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Figure

Helix AI Engineer, Backend

San Jose, CA$200k–$400kmidAdded 4 weeks ago

About this role

Figure AI is seeking a senior backend engineer to design and scale real-time data infrastructure powering their humanoid robot platform. You'll architect high-throughput, low-latency systems handling media and sensor streams from robot fleets, working closely with AI and robotics teams in San Jose with 5 days/week in-office presence.

What you'll do

  • Architect and scale cloud infrastructure for real-time streaming of media and sensor data across robot fleets
  • Design low-latency data pipelines ingesting camera feeds, IMU data, and robot sensor outputs into the AI stack
  • Own reliability, latency, and throughput SLAs for streaming and data infrastructure
  • Integrate ML model serving into real-time data pipelines with AI and robotics teams
  • Build observability, alerting, and monitoring tools for live robot traffic visibility
  • Drive architectural decisions and mentor engineers across the organization

What they're looking for

  • Cloud backend systems scaling (AWS, GCP, or Azure)
  • Real-time data stream processing and low-latency architecture
  • Distributed systems fundamentals
  • Backend languages (Go, C++, Python, or Rust)
  • Containerization and service mesh infrastructure
  • High-bandwidth data transport and connection management
  • AI inference serving integration (nice to have)
  • Streaming protocols like gRPC, Kafka, WebRTC, or RTSP (nice to have)

Benefits

  • Base salary: $150,000 - $400,000 annually
  • Additional compensation components based on role
  • Work on cutting-edge humanoid robotics technology
  • Collaboration with AI and robotics engineering teams
  • High-ownership senior-level position
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Figure

Figure develops advanced humanoid robots powered by AI technology. The company is hiring engineers across mechanical design, firmware development, manufacturing, quality assurance, and security to build and refine its autonomous robotic systems.

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Likely interview questions

  • Walk us through a high-throughput, low-latency system you've built at scale. What were the key architectural decisions, and how did you handle bottlenecks?
  • How would you design a real-time data pipeline to ingest and route high-bandwidth sensor streams (camera, IMU, etc.) from multiple robots while meeting strict latency SLAs?
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