Leads engineering teams to develop and deploy core algorithms transforming physiological sensor data into real-time intelligence for WHOOP wearables. Defines technical roadmap, manages cross-functional execution, and balances innovation with product delivery, requiring ML, signal processing, and embedded systems expertise.
Salary not listed
On-siteEngineering Management
About the role
Responsibilities
Lead and scale high-performing teams of machine learning and signal processing engineers, including senior ICs and tech leads
Define and drive the roadmap for core algorithm systems and on-device intelligence
Guide key architectural and technical tradeoffs across accuracy, latency, power, and system constraints
Deliver end-to-end sensor-driven features from research through production deployment
Drive cross-functional execution across Firmware, WHOOP Labs, and Product; align stakeholders and manage complex dependencies
Establish best practices for algorithm development, validation, integration, and lifecycle management
Balance long-term innovation with near-term product delivery
Develop technical leaders and foster a culture of execution excellence
Qualifications
Proven experience leading engineering teams in machine learning, signal processing, or embedded systems, including managing senior ICs or tech leads
Strong track record of delivering sensor-driven algorithms or systems into production environments
Experience working across hardware, firmware, and data science boundaries, with a systems-level mindset
Demonstrated ability to define and communicate technical strategy and guide teams through ambiguity
Experience driving cross-functional programs, aligning stakeholders, and influencing without direct authority
Strong communication skills, with the ability to translate complex technical concepts to diverse audiences
Passion for building teams, developing talent, and scaling engineering organizations
Preferred Qualifications
Experience with physiological sensing (e.g., PPG, IMU, ECG) or wearable technologies
Experience optimizing models for edge deployment (e.g., quantization, latency, power constraints)
Background in health, fitness, or consumer hardware products
Experience balancing long-term innovation vs. near-term product delivery
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