Engineer

• Own the full ML lifecycle: data collection, cleaning, model training, evaluation, deployment.
• Develop and optimize models for signal processing and time-series biosignals.
• Refactor Python-based models into C/C++ for edge deployment.
• Address resource constraints: memory, threading, concurrency, power.
• Work closely with firmware/software teams to integrate models on-device.
• Ensure production success: deploy, monitor drift, iterate post-launch.
• Lead development of ML models powering physiological signal analysis; take models from research to real-world edge deployment.

Senior ML/Edge Engineer, Signals, Health Care

Interview Engineer

Machine Learning Engineer – Agentic Focus

Voice Engineer

Consulting Engineer – Customer Delivery, Enterprise