Senior Edge Signal Processing Engineer

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 seeking a Senior Edge Signal Processing Engineer to join the Sensor Intelligence Group (SIG), a cross-functional team collaborating across WHOOP Labs, Firmware, and Machine Learning and Research. This role is central to developing next-generation sensor processing to enable foundational health features at WHOOP. You’ll develop the edge signal processing underpinning WHOOP physiological insights and coaching, ultimately delivering meaningful and personalized feedback to millions of members.

Responsibilities

• Lead the design and development of real-time signal processing and edge inference for consumer products

• Own the full development lifecycle, from prototyping and validation through productization and continuous improvement

• Evaluate commercial solutions, develop prototypes and demos, and inform build/buy decisions

• Collaborate cross-functionally with hardware, software, and product teams to bring algorithms to WHOOP members

• Mentor junior engineers and contribute to a high-performance, research-to-production culture

Qualifications

• B.S. in Electrical Engineering or related field, M.S. or PhD preferred

• 5+ years of experience developing and deploying real-time sensor signal processing algorithms (physiological, audio, motion, or related modalities)

• Strong foundation in signal processing fundamentals (e.g., filtering, spectral analysis, time-frequency methods)

• Demonstrated experience integrating signal-processing components into end-to-end embedded systems

• Ability to implement and optimize signal-processing algorithms for constrained compute, memory, latency, and power budgets

• Experience integrating third-party libraries, frameworks, or algorithm IP

• Proficiency in Python or MATLAB for algorithm prototyping and with C/C++ for deployment to embedded product platforms

• Excellent communication skills, including the ability to present technical results to a diverse audience

• Demonstrated creativity, adaptability, and a strong interest in translating prototypes into products

Additional Desired Experience

• Experience deploying signal processing algorithms to battery-powered consumer product platforms

• Familiarity with adaptive signal processing, multi-sensor processing, signal enhancement, detection, or classification techniques

• Experience developing and evaluating hybrid DSP/ML systems

• Familiarity with embedded firmware, RTOS environments, and real-time processing pipelines

• Experience using optimized MCU/DSP libraries and acceleration frameworks (e.g., CMSIS-DSP, CMSIS-NN, or vendor-specific libraries)

• Experience with fixed-point implementation, quantization, or other numerical optimization techniques for embedded signal processing

B.S. in Electrical Engineering or related field, M.S. or PhD preferred
5+ years of experience developing and deploying real-time sensor signal processing algorithms (physiological, audio, motion, or related modalities)
Strong foundation in signal processing fundamentals (e.g., filtering, spectral analysis, time-frequency methods)
Demonstrated experience integrating signal-processing components into end-to-end embedded systems
Ability to implement and optimize signal-processing algorithms for constrained compute, memory, latency, and power budgets
Experience integrating third-party libraries, frameworks, or algorithm IP
Proficiency in Python or MATLAB for algorithm prototyping and with C/C++ for deployment to embedded product platforms
Excellent communication skills, including the ability to present technical results to a diverse audience
Demonstrated creativity, adaptability, and a strong interest in translating prototypes into products

Experience deploying signal processing algorithms to battery-powered consumer product platforms
Familiarity with adaptive signal processing, multi-sensor processing, signal enhancement, detection, or classification techniques
Experience developing and evaluating hybrid DSP/ML systems
Familiarity with embedded firmware, RTOS environments, and real-time processing pipelines
Experience using optimized MCU/DSP libraries and acceleration frameworks (e.g., CMSIS-DSP, CMSIS-NN, or vendor-specific libraries)
Experience with fixed-point implementation, quantization, or other numerical optimization techniques for embedded signal processing