Level

Whoop

Senior Applied ML Algorithm Engineer

Whoop is hiring a Senior Applied ML Algorithm Engineer in Boston, United States. It pays $150k-$215k a year and Level rates it ; you can apply on Level.

AI in this role

pytorchtensorflowscikit-learn

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. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep.

We are seeking a Senior Applied ML Algorithm Engineer to develop and deploy signal processing algorithms and machine learning models that transform raw sensor data into accurate, reliable, and real-time physiological insights on WHOOP devices. In this hands-on role, you will own algorithm development from data analysis, model training, and Python prototyping through efficient C/C++ implementation, firmware integration, and production validation. Working closely with Data Science, Firmware, Hardware, and domain experts, you will deliver robust on-device algorithms that improve the member experience while meeting the power, memory, compute, and latency constraints of wearable hardware.

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.

How we rate this

Senior Applied ML Algorithm Engineer at Whoop rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

PyTorchTensorFlowscikit-learn

Questions you could be asked

  1. What's a project where you used PyTorch hands-on?
  2. Walk me through how you've used TensorFlow in your day-to-day work.
  3. What are the limits of scikit-learn that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: PyTorch, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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