Machine Learning Engineer

An AI-powered HealthTech startup building contactless remote patient monitoring solutions for senior care and healthcare providers. The platform combines intelligent hardware and AI-driven software to continuously monitor health, detect potential risks, and enable proactive care through real-time insights and alerts. Build classical ML models such as XGBoost, ensembles, anomaly detection, and time-series methods for fall detection, vitals monitoring, and health risk scoring. Engineer features from raw, sparse, and noisy radar signal data, point-cloud data, and time-series sensor streams. Contribute to CV-adjacent work such as pose, skeleton, movement, and activity estimation from radar data. Build data pipelines on Databricks for training, evaluation, and inference workflows. Perform exploratory data analysis on resident, device, alert, and facility-level data to identify patterns, edge cases, and model improvement opportunities. Own model evaluation for a safety-critical system, including precision, recall, sensitivity, specificity, false alarms, missed events, and detection latency. Analyze production model behavior across facilities, residents, devices, and time periods. Work with noisy real-world data, including missing values, label quality issues, device variation, sparse events, and facility-specific patterns. Write clean, modular, tested Python code for ML training, evaluation, feature engineering, and inference. Deploy, monitor, and improve models in production. Work closely with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability. We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data. You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production. This role is ideal for someone who is strong in classical ML, comfortable with messy real-world sensor data, and able to write clean production-grade code. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.