Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.
We're looking for a senior-level backend engineer who has scaled high-throughput, low-latency data systems and has strong instincts around cloud infrastructure and real-time streaming pipelines. You'll architect and build the core backend systems that power Figure's real-time data infrastructure — enabling the scale and reliability that our AI and robotics platforms depend on.
This is a high-ownership role at the intersection of media and sensor data streaming, cloud systems, and applied ML serving. You'll work closely with our AI and robotics teams to ensure latency, reliability, and throughput meet the demands of real-world robot operation.
WHAT YOU'LL DO
- Architect and scale cloud backend infrastructure for high-concurrency, real-time streaming of media and sensor data across robot fleets and user sessions.
- Design and build low-latency data pipelines that ingest, route, and process high-bandwidth streams — including camera feeds, IMU data, and other robot sensor outputs — into our AI stack in real time.
- Own reliability, latency, and throughput SLAs for streaming and data infrastructure.
- Collaborate with AI and robotics teams to integrate ML model serving into real-time data pipelines.
- Build observability, alerting, and tooling to give the team full situational awareness over live robot traffic.
- Drive architectural decisions and mentor engineers across the team.
WHAT WE'RE LOOKING FOR
- Deep experience scaling cloud backend systems handling high-concurrency, real-time data streams — media, sensor, telemetry, or equivalent high-bandwidth pipelines.
- Strong fundamentals in distributed systems: stream processing, connection management, data transport, and low-latency architecture.
- Proficiency in one or more backend languages (Go, C++, Python, Rust) and cloud platforms (AWS, GCP, or Azure).
- Experience with containerized infrastructure, service mesh, and large-scale deployment pipelines.
- Strong communication and cross-functional collaboration skills.
NICE TO HAVE
- Hands-on experience integrating AI inference serving (Triton Inference Server, TensorRT, SageMaker, or similar) into real-time data pipelines.
- Background in robotics, autonomous vehicles, live media platforms, or other latency-critical streaming domains.
- Familiarity with protocols such as WebRTC, RTSP, gRPC, or Kafka for real-time data transport.
- Experience with on-device or edge inference and the tradeoffs of cloud vs. edge processing.
The US base salary range for this full-time position is between $150,000 - $400,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.