Senior Machine Learning Scientist (Sensor Intelligence)

Responsibilities

• Research, prototype, and productize lightweight deep learning models suitable for resource-constrained edge targets Drive deep learning model customization and compression strategies such as distillation, pruning, fine-tuning, and quantization-aware training Collaborate with product teams to define member experience targets and with cloud-focused machine learning teams to implement distributed AI systems Lead build/buy decisions by evaluating commercial and open-source model performance for WHOOP use cases Stay current in Edge AI industry trends and best practices and mentor junior team members QUALIFICATIONS: Bachelor's degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, or a related field
• Master’s or PhD degree preferred 5+ years of experience as a Machine Learning Scientist or similar role with a focus on advanced development, preferably related to voice and/or text-based conversational systems Demonstrated experience training, fine-tuning, and deploying state-of-the-art deep learning architectures to resource-constrained embedded targets Experience pre-training and fine-tuning small language models and/or building natural language understanding (NLU) models than run on resource-constrained targets Experience with cloud platforms (AWS or GCP) and familiarity with modern MLOps practices such as CI/CD, model versioning, monitoring, and observability Strong communication and collaboration skills across cross-functional teams 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 ADDITIONAL DESIRABLE EXPERIENCE: Experience deploying deep learning models to microcontrollers or other resource-constrained edge devices using toolchains such as TFLite/LiteRT or ExecuTorch, and with inference libraries such as CMSIS-NN or CMSIS-DSP Experience developing machine learning models for consumer-facing products Experience building multi-modal datasets, including speech, video, text, or physiological signals, for human-AI interaction Familiarity with time-series foundation models and self-supervised learning methods 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.
• The WHOOP compensation philosophy is designed to attract, motivate
• retain exceptional talent by offering competitive base salaries, meaningful equity
• consistent pay practices that reflect our mission and core values.
• At WHOOP, we view total compensation as the combination of base salary, equity
• benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.
• The U.S. base salary range for this full-time position is $150,000 - $215,000 Salary ranges are determined by role, level, and location.
• Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.
• In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.
• These ranges may be modified in the future to reflect evolving market conditions and organizational needs.
• While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.