Senior Edge Algorithm Integration Engineer

WHOOP
Boston, MA
On-site

Who this role is best for

Strong fit for candidates with embedded systems expertise who translate and optimize algorithms for real-time execution on edge devices.

Best fit for

  • Candidates with a background in embedded systems and algorithm optimization for real-time execution
    — “optimizing signal-processing and machine-learning algorithms for compute, memory, latency, power, and real-time execution”
  • Individuals who have experience with translating high-level reference implementations into production-quality C/C++
    — “Translate Python, MATLAB, and other reference implementations into efficient, production-quality C/C++”
  • Professionals who can bridge algorithmic and firmware development with systems thinking
    — “Strong systems thinking and software integration skills, with the ability to understand complex dependencies”

Things to consider

  • Requires strong debugging skills across algorithm and systems boundaries
    — “profile on-device performance, and debug complex issues spanning algorithms, sensors, firmware, and embedded systems”
  • Candidates should consider their ability to work across multiple engineering teams simultaneously
    — “work across Sensor Intelligence, Firmware, Edge ML Platform, Mobile, Cloud, and Connectivity teams”

How to stand out

  • Highlight experience with embedded optimization techniques like quantization or model compression
    — “Experience with one or more relevant embedded optimization technologies or techniques”
  • Emphasize your ability to build reusable tools and test harnesses for edge algorithm deployment
    — “Build reusable tools, test harnesses, profiling infrastructure, and deployment patterns”
  • Demonstrate a track record of resolving cross-platform dependencies for on-device execution
    — “drive the technical work required to resolve or redesign dependencies for on-device execution”
  • Showcase your ability to establish functional and numerical equivalence between reference and edge implementations
    — “Establish functional and numerical equivalence between reference and edge implementations”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • ported and optimized Sensor Intelligence algorithms for WHOOP devices
  • integrated algorithms with firmware and sensor pipelines
Typical background
BS/MS in Computer Engineering, Electrical Engineering, Computer Science, or a related technical field

Skills & requirements

Required

C/c++Embedded SystemsSignal ProcessingMachine LearningAlgorithm OptimizationFirmware IntegrationSensor PipelinesProfilingDebugging

Preferred

DSP Technologies

Stack & domain

C/c++Embedded SystemsSignal ProcessingMachine LearningAlgorithm OptimizationProblem SolvingTeamworkCommunicationLeadershipWearable TechnologyHealth MonitoringEdge Computing

About the role

Original posting from WHOOP via Ashby

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.

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.

Source: WHOOP careers (Ashby)

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