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ashby

Senior Edge Algorithm Integration Engineer

Whoop
Boston, USA
Full-time
Senior
Onsite
Discovered 4 weeks ago
matlabpythontensorflow

Full Job Posting

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.
  • Strong systems thinking and software integration skills, with the ability to understand complex dependencies and collaborate across algorithm, firmware, platform, and other engineering teams to drive technical issues to resolution.
  • Experience with one or more relevant embedded optimization technologies or techniques, such as DSP optimization, fixed-point implementation, quantization, model compression, ARM Cortex-M, DSPs, NPUs, RTOS environments, CMSIS-DSP/CMSIS-NN, or TensorFlow Lite Micro.
  • Experience with physiological sensing, wearable devices, time-series sensor data, low-power systems, or establishing functional and numerical equivalence between reference and embedded implementations is a plus.
  • Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
  • Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
  • WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Preferred Qualifications

  • Experience with physiological sensing, wearable devices, time-series sensor data, low-power systems, or establishing functional and numerical equivalence between reference and embedded implementations is a plus.

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