Machine Learning Engineer for Digital Twin Platform

Company Overview

We're an early-stage startup building a Digital Twin platform for physical therapists and fitness professionals, turning wearable IMU and PPG sensor data into actionable movement insight.

Role Summary

We're looking for a machine learning engineer to lead the modeling pipeline for our Digital Twin platform — from raw IMU/PPG signal processing to motion classification and intent prediction. You'll be a key technical contributor helping us turn wearable sensor data into a tool clinicians and coaches can trust.

Responsibilities

• Lead the development and optimization of the modeling pipeline, from signal preprocessing to advanced motion classification.
• Implement and refine an SVM-based classifier for distinguishing known and unknown movements.
• Develop and integrate a LASSO and neural network model (LSTM/CNN) for accurate motion intent prediction.
• Collaborate with a cross-functional team to ensure the platform meets the needs of clinicians and fitness professionals.
• Contribute to transforming raw sensor data into reliable and actionable tools for end users.

Required Skills

• Strong expertise in machine learning techniques, particularly SVM and neural networks such as LSTM and CNN.
• Proficiency in signal preprocessing methods and data analysis.
• Experience with wearable sensor data, specifically IMU and PPG sensors.
• Ability to develop algorithms that provide actionable insights for healthcare and fitness applications.

Nice to Have

• Background in physical therapy or sports science to better align technical solutions with user needs.
• Experience in early-stage startups or innovative tech environments.