Machine Learning Engineer

CodeRound; Hinoba-an; On-site; PHP 800,000 - 1,400,000; Full time

Job summary

CodeRound is seeking a Machine Learning Engineer to build and productionize ML models for radar-derived health insights in a HealthTech startup environment. You will work closely with hardware, data engineering, backend, and product teams to improve accuracy and deploy reliable ML systems across facilities.

The role emphasizes handling messy sensor data, writing production-grade Python code, and delivering safety-critical ML performance in real-world settings.

Qualifications

• Must be strong in classical ML and able to handle messy real-world sensor data.
• Ability to write clean, production-grade Python code for ML training, evaluation, and inference.
• Experience building models for radar/sensor data and health-related insights.

Responsibilities

• Build classical ML models (XGBoost, ensembles, anomaly detection, time-series) for fall detection and vitals monitoring.
• Engineer features from raw radar signal data, point-cloud data, and time-series streams.
• Contribute to CV-adjacent work like pose and movement 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 and edge cases.
• Own model evaluation for a safety-critical system (precision, recall, latency, false alarms).
• Analyze production model behavior across facilities, residents, devices, and time periods.
• Work with noisy real-world data (missing values, label quality issues, device variation).
• Write clean, modular, tested Python code for ML training, evaluation, feature engineering, and inference.
• Deploy, monitor, and improve models in production.
• Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

Skills

Classical ML

Production-grade code

Radar data analysis

Data pipelines

Python

Tools

Databricks

Job description

Client: 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.

Requirements:

We are looking for a Machine Learning Engineer to build and productionize models thatpower fall detection, vitals monitoring, and predictive health insights from radar sensor data.You will work closely with hardware, data engineering, backend, and product teams toimprove model accuracy, reduce false alarms, and deploy reliable ML systems intoproduction.This role is ideal for someone who is strong in classical ML, comfortable with messyreal-world sensor data, and able to write clean production-grade code.

Responsibilities:

• 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.