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The Sensor Intelligence Engineer will design and optimize machine learning algorithms for edge devices, collaborating with cross-functional teams on innovative solutions while enhancing biosensor systems.
At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
As a Sensor Intelligence Engineer at WHOOP, you will be part of a cross-functional team of Sensor Intelligence/Signal Processing Engineer, WHOOP Labs, Firmware Engineers, and Data Scientists. You will work on core, fundamental features at WHOOP, tasked with solving the incredibly difficult technical challenge of obtaining physiological information from noisy sensor data, enhancing diagnostic tools, and designing scalable algorithms deployable on constrained edge devices.
RESPONSIBILITIES:
- Design, optimize and maintain machine learning algorithm on the edge device
- Collaborate closely with Data Science, Firmware, and Research teams to enhance user metrics by developing innovative and efficient algorithms that are deployable on low-power embedded systems.
- Design, prototype, and implement machine learning solutions that run on edge devices with limited compute, memory, and power budgets.
- Participate in the full software development lifecycle, including development, debugging, hardware-in-the-loop testing, and deployment on edge platforms.
- Leverage expertise in signal processing, time-series analysis, and embedded ML to optimize biosensor systems and improve inference accuracy at the edge.
- Explore, model, and implement algorithms that balance performance and power efficiency while maintaining scalability and adaptability.
- Contribute to research efforts exploring new features, hardware-aware model optimization, and intelligent data processing pipelines for edge deployment.
QUALIFICATIONS:
- Bachelor’s or Master’s degree in applied mathematics, electrical/biomedical engineering, computer engineering, or a related field.
- 2+ years of industry or research experience in signal processing and/or machine learning, preferably with deployment experience on embedded or wearable platforms.
- Understanding of biosensor systems and analysis of physiological signals in noisy, real-world conditions.
- Strong programming proficiency in C and/or Python
- Experience developing and optimizing machine learning models for edge devices including model quantization, pruning, or lightweight inference.
- Working knowledge of adaptive signal processing, real-time systems, and time-series analysis.
- Deep understanding of ML libraries such as TensorFlow Lite, scikit-learn, PyTorch, or TinyML frameworks.
- Excellent communication skills, both written and oral, with a track record of conveying complex technical topics to diverse teams.
- Demonstrated creativity, adaptability, and a passion for building impactful products that scale to real-world, edge-deployable use cases.
Join us in pushing the boundaries of wearable technology and positively impacting people's lives!
This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
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.
Top Skills
C
Python
PyTorch
Scikit-Learn
Tensorflow Lite
Tinyml Frameworks
WHOOP Boston, Massachusetts, USA Office
1 Kenmore Sq, Boston, MA, United States, 02215
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