# Senior Machine Learning Engineer (Sensor Intelligence)

**Company:** [WHOOP](https://hotfix.jobs/companies/whoop)
**Location:** Boston, MA
**Role:** ML Engineering
**Experience:** 4+ years
**Skills:** Python, PyTorch, TensorFlow, Deep Learning, Machine Learning, Time Series Analysis, Signal Processing, AWS, GCP, Foundation Models
**Posted:** 2026-01-09

> Design and deploy deep learning models on biosensor and time-series data to deliver personalized health metrics. Requires strong ML/DL fundamentals, publications, and 4+ years of research experience.

## Job Description

## 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 factors such as 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 either Computer Science, Electrical engineering, Biomedical engineering, Data Science, Artificial Intelligence, Statistics, or a related field.
- Must have published research papers in ML/DL domains, preferably application of ML/DL on biomedical data.
- Solid understanding of ML fundamentals, and particularly DL techniques, including the mathematics behind the algorithms.
- 4+ years of work/academic experience as a Machine-Learning/Deep-Learning researcher (2+ years post-PhD for PhD holders).
- Experience developing or supporting regulated or high-risk ML systems (e.g., digital health, software as a medical devices) is a plus.
- Strong experience with time series data (wearables, physiological signals, high-frequency sensor data) and signal processing.
- Strong experience with multiple DL architectures; experience training/fine-tuning/deploying Foundation AI models is a plus.
- Proficiency in Python (scientific stack), ML/DL frameworks (PyTorch, TensorFlow).
- Experience with cloud computing platforms (AWS or GCP) is a plus.
- Strong communication and collaboration skills across cross-functional teams.
- Commitment to leveraging AI tools while maintaining high-quality standards.

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