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Senior Applied ML Algorithm Engineer

Whoop
Boston, USA
Full-time
Senior · 5+ years experience
Onsite
Discovered 2 weeks ago
pythonpytorchscikit-learntensorflow

Full Job Posting

RESPONSIBILITIES:

  • Design and develop algorithms that combine signal processing, feature extraction, and machine learning to derive meaningful physiological insights from wearable sensor data.
  • Analyze large-scale, noisy sensor datasets to train and evaluate models, identify performance gaps, and improve accuracy, robustness, and generalization across diverse members and real-world conditions.
  • Translate algorithm prototypes and trained models into production-ready C/C++ implementations, optimizing signal processing pipelines and on-device inference for accuracy, power, memory, compute, and latency.
  • Partner closely with Firmware and Hardware teams to integrate algorithms into production firmware, validate execution on target hardware, and resolve differences between prototype and embedded performance.
  • Define performance metrics and rigorous validation plans, combining offline evaluation, lab-based experiments, real-world data analysis, and on-device testing to assess algorithm accuracy, reliability, and efficiency.
  • Collaborate with Data Science, Software, Product, and domain experts to translate research findings into production-ready capabilities, investigate post-deployment performance gaps, and drive continuous algorithm improvement.

QUALIFICATIONS:

  • 5+ years of experience developing signal processing and machine learning algorithms for time-series or sensor data in real-world applications.
  • MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or a related quantitative field, or equivalent practical experience.
  • Strong foundation in digital and statistical signal processing for noisy time-series data. Experience with physiological signals or wearable sensors is a plus.
  • Proficiency in Python for data analysis, model development, and experimentation, and C/C++ for implementing efficient algorithms in embedded firmware.
  • Experience developing, training, and evaluating machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn.
  • Demonstrated experience deploying machine learning models to resource-constrained embedded systems, including optimization across accuracy, power, memory, compute, and latency.
  • Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs with firmware, hardware, and data science partners.
  • 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.
  • 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.

Preferred Qualifications

  • Strong foundation in digital and statistical signal processing for noisy time-series data. Experience with physiological signals or wearable sensors is a plus.

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