Senior Machine Learning Engineer, Wearable Intelligence

Location: New York, NY
Employment: Full-time | Hybrid / On-site
Salary Range: $130K-$160K + early-stage equity

About the role:

As Lead Machine Learning Engineer, you will help build the intelligence behind Pync’s wearable platform. You will work with real-world sensor data from our devices and lead the development of models that translate raw signals from device sensors into useful behavioral and health-related insights.

This role covers the full machine learning lifecycle, from data collection and annotation to model training, evaluation, deployment, and continuous improvement. You will collaborate with product, engineering, research, and data collection partners across the U.S. and internationally.

Responsibilities:

• Lead architecture, strategy, and technical tradeoffs for Pync’s ML-based wearable intelligence system as an individual-contributor leadership role with high technical ownership OR with possibility to later hire and mentor future ML team members
• Build and improve models using IMU, audio, and other wearable sensor data to understand pet activity, behavior, and wellbeing
• Own the ML pipeline from data collection and annotation to model training, evaluation, deployment, monitoring, and retraining
• Define model evaluation frameworks that measure not only accuracy, but also robustness across pets, environments, devices, and real-world usage conditions
• Collaborate with hardware, firmware, mobile, backend, product, and research teams to ensure models work within real wearable constraints, including signal quality, battery life, latency, and connectivity
• Translate model outputs into useful, trustworthy user-facing insights for pet owners, veterinarians, and partners

Qualifications:

• 5+ years in ML engineering with a focus on time-series or signal processing
• Hands-on experience with IMU data — accelerometer, gyroscope, or similar sensors
• Experience optimizing models for edge deployment and real-time inference
• Familiar with the full machine learning pipeline — from raw sensor data to production-ready models
• Experience with wearable devices or embedded sensor systems is a strong plus
• Background in animal behavior, veterinary science, or biosignal research is a plus
• Loves pets and wants to help shape the future of pet care