Senior Wearable ML & Signal Processing Engineer

Whoop; Boston (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

WHOOP is seeking a Senior Applied ML Algorithm Engineer to design and deploy signal processing and ML models that turn raw sensor data into real-time physiological insights on WHOOP devices. You will implement in Python and production-ready C/C++ with firmware integration, working across Data Science, Firmware, and Hardware teams.

Based in Boston, MA, the role demands 5+ years in signal processing/ML for time-series data and a strong background in embedded systems optimization for wearable

Qualifications

• 5+ years of experience developing signal processing and ML algorithms for time-series or sensor data.
• MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or related field, or equivalent.
• Strong foundation in digital and statistical signal processing for noisy time-series data.
• Proficiency in Python for data analysis and model development, and C/C++ for embedded firmware.
• Experience with TensorFlow, PyTorch, or scikit-learn.
• Experience deploying ML models to resource-constrained embedded systems.
• Ability to investigate complex algorithmic problems and communicate tradeoffs with firmware/hardware teams.
• Commitment to leveraging AI tools in day-to-day tasks with high-quality standards.

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.

Skills

Signal processing

Time-series ML

Python for data analysis

C/C++ for embedded firmware

Education

MS/PhD in EE, Biomed Eng, CS or related

Tools

TensorFlow

PyTorch

scikit-learn

Embedded C/C++ toolchain

Job description

WHOOP is seeking a Senior Applied ML Algorithm Engineer to design and deploy signal processing and ML models that turn raw sensor data into real-time physiological insights on WHOOP devices. You will implement in Python and production-ready C/C++ with firmware integration, working across Data Science, Firmware, and Hardware teams.

Based in Boston, MA, the role demands 5+ years in signal processing/ML for time-series data and a strong background in embedded systems optimization for wearable