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The Role
Design and implement algorithms to extract physiological signals from noisy biosensor data, optimize on-device computation and battery use, deploy and optimize ML models for real-time edge inference, collaborate with firmware, data science, and product teams, and validate production-ready code and clinical-study–driven analytics.
Summary Generated by Built In
Senior Signal Processing Engineer
Boston, MA
Duties:
Whoop, Inc. seeks a Senior Signal Processing Engineer (Boston, MA) to design and implement
algorithms to extract physiological information from noisy biosensor data and optimize on-
device computation. Partner with Firmware and Data Science teams to deploy artificial
intelligence and machine learning models on edge devices. Optimize models for real-time
inference on edge devices, including analysis and improvement of battery consumption patterns.
Collaborate with the Signal Processing team to develop algorithms that personalize calculations
based on member data. Research and design innovative algorithms to improve performance
under edge-computation constraints. Contribute to software development, debugging, and
validation to ensure production-ready code and reliable results. Build, train, and test machine
learning models for large-scale data processing on edge devices. Collaborate with Product
Managers to translate member needs into machine learning–based solutions.
algorithms to extract physiological information from noisy biosensor data and optimize on-
device computation. Partner with Firmware and Data Science teams to deploy artificial
intelligence and machine learning models on edge devices. Optimize models for real-time
inference on edge devices, including analysis and improvement of battery consumption patterns.
Collaborate with the Signal Processing team to develop algorithms that personalize calculations
based on member data. Research and design innovative algorithms to improve performance
under edge-computation constraints. Contribute to software development, debugging, and
validation to ensure production-ready code and reliable results. Build, train, and test machine
learning models for large-scale data processing on edge devices. Collaborate with Product
Managers to translate member needs into machine learning–based solutions.
Requirements:
Master's degree in Computer Science, Applied Mathematics, Electrical Engineering, Biomedical
Engineering, or related field (or foreign degree equivalent) and at least 4 years of experience
with signal processing and/or machine learning. At least 4 years of experience developing and
implementing AI solutions for real-time processing on edge devices; At least 4 years of
experience collaborating with cross-functional teams to understand requirements and design
efficient edge computing solutions; At least 4 years of experience with biosensor systems and
analyzing biomedical data; At least 4 years of experience with signal and image processing
applications (C, C++, Python or MATLAB); At least 4 years of experience with ML libraries
such as scikit-learn, Tensorflow, PyTorch or Keras; At least 4 years of experience with statistical
method and design of clinical studies; At least 4 years of experience with code and battery
optimization on the edge; At least 4 years of experience adapting to changing requirements while
producing high quality reports under tight deadlines. Full-time position.
Engineering, or related field (or foreign degree equivalent) and at least 4 years of experience
with signal processing and/or machine learning. At least 4 years of experience developing and
implementing AI solutions for real-time processing on edge devices; At least 4 years of
experience collaborating with cross-functional teams to understand requirements and design
efficient edge computing solutions; At least 4 years of experience with biosensor systems and
analyzing biomedical data; At least 4 years of experience with signal and image processing
applications (C, C++, Python or MATLAB); At least 4 years of experience with ML libraries
such as scikit-learn, Tensorflow, PyTorch or Keras; At least 4 years of experience with statistical
method and design of clinical studies; At least 4 years of experience with code and battery
optimization on the edge; At least 4 years of experience adapting to changing requirements while
producing high quality reports under tight deadlines. Full-time position.
Skills Required
- Master's degree in CS, Applied Math, EE, Biomedical Eng or related (or foreign equivalent)
- At least 4 years experience with signal processing and/or machine learning
- At least 4 years developing and implementing AI solutions for real-time edge devices
- At least 4 years collaborating with cross-functional teams to design efficient edge computing solutions
- At least 4 years experience with biosensor systems and analyzing biomedical data
- At least 4 years experience with signal and image processing applications using C, C++, Python or MATLAB
- At least 4 years experience with ML libraries such as scikit-learn, TensorFlow, PyTorch or Keras
- At least 4 years experience with statistical methods and design of clinical studies
- At least 4 years experience with code and battery optimization on edge devices
- At least 4 years experience adapting to changing requirements while producing high-quality reports under tight deadlines
What the Team is Saying
Josh
Lead Designer
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Manan Dedhia
Field Quality Manager
Anahis
Staff Tech Lead - Mechanical Engineering
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WHOOP Compensation & Benefits Highlights
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Wellbeing & Lifestyle Benefits — Wellness support includes a stipend and a complimentary WHOOP membership to use and gift, aligning perks with the company’s health focus. Feedback suggests these lifestyle benefits are a meaningful part of total rewards.
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Healthcare Strength — Core coverage spans medical, dental, vision, mental health services, and life and disability insurance. This breadth indicates a comprehensive health safety net.
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Equity Value & Accessibility — Total rewards commonly include stock options or equity participation, positioning ownership as part of compensation. Feedback suggests equity is viewed as a valuable component of the package.
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The Company
What We Do
At WHOOP, we’re on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable device and performance optimization platform has been adopted by many of the world's greatest athletes and consumers alike.
Why Work With Us
At WHOOP, we’re focused on building an inclusive and equitable team with a strong sense of belonging for everyone—increasing representation in every way as our team grows. We believe that our differences are our source of strength—so much so it’s one of our core values.
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WHOOP Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Typical time on-site:
4 days a week
