Senior Machine Learning Research Engineer (Deep Learning, Sensor Intelligence Group)

WHOOP; Boston (MA); Sur place; USD 150 000 - 215 000; Plein temps

Avantages offerts par ce poste

Equity package

Comprehensive benefits

Résumé du poste

WHOOP is seeking a Senior ML Research Engineer in Boston, MA to enhance member health and performance through advanced machine-learning techniques. This role involves designing robust models to analyze time-series biosensor data and ensuring analytical integrity across systems. The ideal candidate will have an advanced degree and extensive experience in ML/DL research, specifically in regulated environments.

The base salary ranges from $150,000 to $215,000, complemented by additional benefits and equity packages.

Qualifications

• 4+ years of experience in ML/DL research.
• Published papers in ML/DL domains, especially with biomedical data.
• Strong understanding of ML fundamentals and DL techniques.

Responsabilités

• Design and train ML models using time-series data.
• Develop documentation for regulated health features.
• Optimize ML models for production deployment.

Connaissances

Deep Learning

Machine Learning

Python

Cross-functional collaboration

Time-series analysis

Formation

Master’s or PhD in Computer Science or related field

Outils

PyTorch

TensorFlow

AWS

GCP

Description du poste

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.

WHOOP is seeking a Senior ML Research Engineer to join the Sensor Intelligence Group (SIG). This role will contribute to both member-facing and regulated health features, requiring a balance of strong rigor in machine‑learning and deep‑learning fundamentals, and clinical‑data/regulatory awareness. You will tackle the complex challenge of extracting reliable insights from noisy sensor data and deploying robust algorithms on constrained edge and cloud environments, ultimately delivering meaningful and personalized metrics to millions of members. Join us in pushing the boundaries of wearable technology and positively impacting people’s lives!

RESPONSIBILITIES

• Design and train deep‑learning (DL) and machine‑learning (ML) models to extract valuable insights from large repositories of time‑series/biosensor data.
• Stay up to date with the latest advancements in DL research and technologies.
• Support documentation of the algorithms for regulated health features.
• Write clean, efficient, and maintainable code.
• Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing any issues related to data and data quality.
• Conduct experiments and perform rigorous testing of the models. Optimize and fine‑tune the DL/ML (including Foundation AI models) models for deployment in production systems, considering computational resources and real‑time constraints. Prepare comprehensive reports for cross‑functional teams.
• Contribute to ongoing research efforts and explore new features for the Whoop product. Collaborate with engineers from SIG, Data Science and Firmware teams to translate research prototypes into scalable, efficient, and cost‑effective ML inference systems.

QUALIFICATIONS

• Master’s or PhD degree in Computer Science, Electrical Engineering, Biomedical Engineering, Data Science, Artificial Intelligence, Statistics, or a related field.
• Published research papers in ML/DL domains, preferably applying ML/DL to biomedical data.
• Solid understanding of ML fundamentals, with a particular focus on DL techniques. Mathematical knowledge of the algorithms is valued.
• 4+ years of work or academic experience as a Machine‑Learning/Deep‑Learning researcher (2+ years post‑PhD work experience for PhD holders). Requirements may be relaxed for exceptional candidates.
• Experience developing or supporting regulated or high‑risk ML systems (e.g., digital health, software as a medical device), including familiarity with validation, documentation, and change‑management in regulated environments.
• Strong experience with time‑series data, e.g., wearable and physiological signals or high‑frequency sensor data. Familiarity with signal‑processing concepts and techniques is expected.
• Strong experience with multiple DL architectures. Experience training, fine‑tuning, or deploying Foundation AI models is a plus.
• Proficiency in Python (scientific stack) and ML/DL frameworks such as PyTorch or TensorFlow.
• Experience with cloud computing platforms (e.g., AWS or GCP) is a plus.
• Excellent written and oral communication and collaboration skills across cross‑functional teams.
• Commitment to embracing and leveraging AI tools in day‑to‑day tasks, ensuring AI‑assisted work meets the same high‑quality standards as personal contributions.
• Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing quality reports under tight deadlines.

LOCATION

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.

LEGAL STATEMENTS

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.

COMPENSATION & BENEFITS

U.S. base salary range: $150,000 – $215,000, determined by role, level, and location. Base pay is based on job‑related skills, experience, performance, and relevant education or training.

In addition to the base salary, the successful candidate will 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.