Senior Edge Algorithm Integration Engineer

WHOOP; Boston (MA); On-site; USD 120,000 - 180,000; Full time

Job summary

WHOOP is seeking an Edge Algorithm Integration Engineer to bring Sensor Intelligence algorithms from reference implementations to production on WHOOP devices. You will translate and optimize algorithms for embedded hardware and collaborate across Sensor Intelligence, Firmware, Edge ML Platform, Mobile, Cloud, and Connectivity teams.

You will ensure numerical equivalence, profile performance on device, and build reusable tools to accelerate future migrations, balancing compute, memory, latency

Qualifications

• BS/MS in Computer Engineering, Electrical Engineering, Computer Science, or a related technical field.
• Strong C/C++ software development experience with embedded, real-time, or resource-constrained systems, including memory management, compute, latency, and power considerations.
• Experience porting, integrating, or optimizing signal-processing or machine-learning algorithms for MCU-based, embedded, or edge platforms, including translating Python, MATLAB, or similar reference environments.
• Experience profiling and optimizing embedded software for runtime, memory footprint, computation, and/or power, with strong debugging across algorithm and systems boundaries.
• Strong systems thinking and software integration skills for cross-team collaboration to resolve technical issues.
• Experience with DSP optimization, fixed-point implementation, quantization, model compression, ARM Cortex-M, DSPs, NPUs, RTOS, CMSIS-DSP/CMSIS-NN, or TensorFlow Lite Micro.
• Experience with physiological sensing, wearable devices, time-series sensor data, low-power systems, or verifying algorithm equivalence is a plus.
• Commitment to leveraging AI tools in day-to-day tasks with high-quality output.

Responsibilities

• Own end-to-end porting of Sensor Intelligence algorithms from reference environments to production-ready implementations on WHOOP embedded platforms.
• Translate Python, MATLAB, and other references into efficient production-quality C/C++, optimizing signal-processing and ML algorithms for compute, memory, latency, power, and real-time execution.
• Understand architecture, data flow, runtime requirements, and dependencies across cloud, mobile, connectivity, and sensor pipelines; resolve dependencies for on-device execution.
• Integrate algorithms with firmware, sensor pipelines, and Edge ML platform capabilities, defining interfaces, requirements, and system behavior.
• Establish numerical equivalence between reference and edge implementations, profile on-device performance, and debug complex issues spanning algorithms and hardware.
• Build reusable tools, test harnesses, profiling infrastructure, and deployment patterns to accelerate future migrations across WHOOP hardware.

Skills

C/C++ development

Embedded/RTOS systems

Algorithm porting to edge

Performance profiling

Debugging across boundaries

Education

BS/MS in Computer Engineering, Electrical Engineering, CS

Tools

CMSIS-DSP/CMSIS-NN

TensorFlow Lite Micro

ARM Cortex-M

DSP optimization techniques

Job description

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.

• 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.

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.