Senior Backend Software Engineer, CHAOS Industries

• We're looking for builders who learn quickly and enjoy hard distributed systems and real-time data problems
• We're looking for exceptional software engineers who are excited by hard technical problems and building systems that matter
• Beyond our core stack, we care more about strong engineering fundamentals, curiosity, and the ability to learn quickly than about any specific technology
• Have strong production experience building backend services in Python (FastAPI or similar)
• Have built real-time or streaming data systems - message queues, WebSockets, time-series data - and enjoy hard distributed systems problems
• Have owned a production system end to end - designed it, shipped it, and fixed it when it broke
• Are eager to learn new technologies and adapt to new technical domains
• Care deeply about reliability, security, and scalability
• Get real leverage from AI coding tools, with a considered view on what to delegate to them and what not to
• Communicate well and enjoy collaborating across teams
• Ability to obtain and maintain a U.S. security clearance

Desirable

• You'll join a small, highly collaborative team in San Francisco building the software behind some of the world's most advanced radar systems
• Working alongside software, hardware, and systems engineers, and the teams who take these systems into the field, you'll design the backend services and data pipelines that turn raw sensor data into decisions people can act on
• Edge-to-Cloud Data Delivery - robust, low-latency systems that move sensor data from radars in the field, often over constrained bandwidth, to the services and operators that depend on it
• Backend Services - high-performance Python (FastAPI) services that ingest, process, and distribute real-time sensor data reliably and securely at scale
• Live and Replay Data - the streaming, storage, and replay of detections, tracks, and telemetry that power high-speed live views and playback of 3D geospatial data fused from multiple sensors
• Fleet Orchestration - the coordination and decision-making layer that optimizes how a growing fleet of sensors operates as conditions in the field change
• Platform Architecture - help shape the technical direction of the platform, making architectural decisions that support new capabilities, increasing scale, and long-term maintainability