Principal Software Engineer

Location: San Francisco

WHAT YOU’LL DO

1. Act as the technical lead for large parts of the scanner platform: system architecture, codebase structure, and long-term maintainability.

2. Own core runtime foundations: distributed control, state management, fault handling, and reliability.

3. Drive engineering rigor: testability, code quality, review standards, performance regression prevention, and release processes.

4. Build robust observability: logs, metrics, traces, and replayable diagnostics (with privacy constraints).

5. Collaborate with hardware and recon/ML teams to define interfaces, data contracts, timing/synchronization, and failure modes.

6. Lead complex refactors (e.g., message passing / RPC boundaries, modularization, concurrency model) without halting forward progress.


WHAT WE’RE LOOKING FOR

- Deep software architecture experience for real-world systems: robotics, instrumentation, medical devices, or other complex distributed products.

- Strong Python and concurrency background (asyncio, multiprocessing, profiling, performance engineering).

- Track record of shipping systems that are observable, debuggable, and resilient.

- Strong technical leadership: clarity, pragmatic trade-offs, and mentoring.


USEFUL EXPERIENCE

- Building but rock-solid systems: clear interfaces (gRPC/protobuf or equivalent), strong state modeling, and failure handling.

- High-leverage engineering habits on a lean team: good tests, CI, reproducible dev environments, and fast code review.

- Practical performance + concurrency work in Python (asyncio, profiling, multiprocessing) and comfort debugging distributed behavior.

- Security-minded device software: safe defaults, encrypted data paths, and disciplined handling of PII/PHI.

- Operational thinking: remote updates/management, excellent logging, and diagnostics that make real hardware debuggable.

Responsibilities

Act as the technical lead for large parts of the scanner platform: system architecture, codebase structure, and long-term maintainability; Own core runtime foundations: distributed control, state management, fault handling, and reliability; Drive engineering rigor: testability, code quality, review standards, performance regression prevention, and release processes; Build robust observability: logs, metrics, traces, and replayable diagnostics (with privacy constraints); Collaborate with hardware and recon/ML teams to define interfaces, data contracts, timing/synchronization, and failure modes; Lead complex refactors (e.g., message passing / RPC boundaries, modularization, concurrency model) without halting forward progress

Requirements

Deep software architecture experience for real-world systems: robotics, instrumentation, medical devices, or other complex distributed products; Strong Python and concurrency background (asyncio, multiprocessing, profiling, performance engineering); Track record of shipping systems that are observable, debuggable, and resilient; Strong technical leadership: clarity, pragmatic trade-offs, and mentoring

Nice to have

Building but rock-solid systems: clear interfaces (gRPC/protobuf or equivalent), strong state modeling, and failure handling; High-leverage engineering habits on a lean team: good tests, CI, reproducible dev environments, and fast code review; Practical performance + concurrency work in Python (asyncio, profiling, multiprocessing) and comfort debugging distributed behavior; Security-minded device software: safe defaults, encrypted data paths, and disciplined handling of PII/PHI; Operational thinking: remote updates/management, excellent logging, and diagnostics that make real hardware debuggable