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# Staff Software Engineer - Sensing

- **Company:** SNTNL LLC
- **Location:** San Francisco, CA, United States
- **Salary:** $200,000 - $225,000
- **Contract:** Permanent contract
- **Skills:** Training Data, C (Programming Language), Geographic Information Systems, Application Programming Interfaces (APIs), UClibc (C Standard Library), C++ (Programming Language), Software as a Service, Software Quality, Communications Protocols, Software Debugging, DevOps, Digital Electronics, Distributed Systems, Linux on Embedded Systems, Fault Tolerance, Firmware, FreeRTOS, Python (Programming Language), Machine Learning, Real-Time Operating Systems, Cloud Services, Service-Oriented Architecture, Signal Processing, Universal Asynchronous Receiver/Transmitter, Diagnostic Tools, Cloud Platform System, Real Time Systems, Backend, Event Driven Architecture, Yocto, Information Technology, Front End Software Development, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_42ab60bcffdf90a09afc93d4d7806a05

## About the Role

* You enjoy building and thrive in teams that care a lot about people.
 * Deep proficiency in Rust, C, C++, Python with practical experience in RTOS (e.g., Zephyr, FreeRTOS) and Embedded Linux (Yocto, Buildroot).
 * Intuitive grasp of digital electronics, board-level bring-up/validation/debugging, BSP, and peripheral communication (SPI, I²C, UART, interrupt-driven design).
 * Ability to troubleshoot complex systems using oscilloscopes, logic analyzers, and diagnostic tools at the hardware/software boundary.
 * Experience building deterministic, timing-sensitive firmware that integrates effectively with cloud-native data pipelines.
 * Find extraordinarily creative ways to amass training data quickly to develop production-grade algorithms.
 * Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related field., * Experience in applied signal processing, machine learning
 * Experience in healthcare, GIS, safety-critical, or high-availability environments
 * Experience scaling SaaS platforms through periods of rapid growth
 * Familiarity with event-driven architectures, real-time systems, or location-aware platforms
 * Familiarity or interest in contributing to:
 + Distributed systems, APIs, and service-oriented architectures
 + Modern full-stack application development
 + DevOps, SRE, or platform engineering practices

## Description

We build technology that makes healthcare environments safer for the people delivering care. Our connected safety platform powers real-time communication, response coordination, and location intelligence across hospitals and complex care environments. We operate in high-trust, high-reliability environments where software quality, operational resilience, and customer experience are paramount. Our engineering organization is scaling alongside our growth, and we are looking for a Staff Software Engineer who can help shape both our technical direction and the way we build., Develop our next-generation sensing data pipeline, building the "physical full stack" from firmware and edge algorithms to the backend and frontend services that power them. Bridge physical hardware and cloud systems, applying physics-based modeling to solve complex sensing challenges at scale. Shape architecture, influence engineering standards, and help scale a platform, organization, and culture simultaneously., * Drive development of next-generation hardware used by millions of healthcare professionals

 * Design and implement robust firmware across Canopy devices; build and maintain an HIL automation platform to accelerate development and enforce system safety
 * Apply signal processing and machine learning for on-device algorithms, cloud pipelines, and field-operations tools
 * Collaborate with hardware product and software teams to launch production-grade sensing devices at scale
 * Apply systems-level design intuition to compute layers power management and communication protocols
 * Engineer industrial-grade fault-tolerant designs to ensure reliability during unexpected power events