Senior Edge Algorithm Integration Engineer

About the role

At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.

WHOOP is hiring an Edge Algorithm Integration Engineer to join the Edge ML team and bring Sensor Intelligence algorithms from validated reference implementations to production execution on WHOOP devices. You will own the technical path from reference algorithm to edge deployment, translating and optimizing algorithms for embedded hardware, resolving cross-platform dependencies, integrating with firmware and sensor infrastructure, and ensuring implementations preserve expected algorithm performance. You will work across Sensor Intelligence, Firmware, Edge ML Platform, Mobile, Cloud, and Connectivity teams to enable algorithms to run reliably and efficiently within the compute, memory, latency, and power constraints of WHOOP devices.

RESPONSIBILITIES:

- Own the end-to-end technical path for porting developed and validated Sensor Intelligence algorithms from reference environments to production-ready implementations on WHOOP embedded platforms.

- Translate Python, MATLAB, and other reference implementations into efficient, production-quality C/C++, optimizing signal-processing and machine-learning algorithms for compute, memory, latency, power, and real-time execution.

- Understand algorithm architecture, data flow, runtime requirements, and dependencies across cloud, mobile, connectivity, sensor pipelines, libraries, and platform services; drive the technical work required to resolve or redesign dependencies for on-device execution.

- Integrate algorithms with firmware, sensor pipelines, embedded services, and Edge ML platform capabilities, partnering closely with Sensor Intelligence and Firmware teams to define interfaces, requirements, acceptance criteria, and system-level behavior.

- Establish functional and numerical equivalence between reference and edge implementations, profile on-device performance, and debug complex issues spanning algorithms, sensors, firmware, and embedded systems.

- Build reusable tools, test harnesses, profiling infrastructure, and deployment patterns that accelerate future algorithm migrations and enable efficient portability across current and next-generation WHOOP hardware platforms.

QUALIFICATIONS:

- BS/MS in Computer Engineering, Electrical Engineering, Computer Science, or a related technical field, or equivalent practical experience.

- Strong C/C++ software development experience with embedded, real-time, or resource-constrained systems, including an understanding of memory management, compute limitations, latency, and power constraints.

- Experience porting, integrating, or optimizing signal-processing or machine-learning algorithms for MCU-based, embedded, or edge platforms, including translating implementations from Python, MATLAB, or similar reference environments.

- Experience profiling and optimizing embedded software for runtime, memory footprint, computational efficiency, and/or power, with strong debugging skills across algorithm and systems boundaries.