AI/ML Engineer Intern
Make My Health is hiring an AI/ML engineer intern to turn wearable and health-sensor data (Apple HealthKit, Google Fit, custom BLE devices) into models for posture detection, activity classification, and health-risk scoring. Day-to-day spans Python/PyTorch/TensorFlow work, time-series EDA, and building pipelines for on-device or cloud inference.
- Compensation
- ₹5K–₹10K
- Location
- Noida
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Degree (or equivalent experience) in Computer Science, Data Science, Statistics, Biomedical Engineering, or a related field
- Strong Python skills and experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
- Experience working with time-series or sensor data (wearable/health data a plus; general time-series/IoT/signal-processing also counts)
- Comfortable with the full ML lifecycle: data cleaning, feature engineering, model training/evaluation, and deployment
- Understanding of basic signal-processing techniques (filtering, sensor calibration, feature extraction from accelerometer/gyroscope/heart-rate data)
- Ability to work with ambiguous, early-stage product requirements and iterate quickly
Nice to have
- Familiarity with health data APIs/SDKs (Apple HealthKit, Google Health Connect, or similar), or willingness to learn quickly
- Experience with edge/on-device ML (TensorFlow Lite, Core ML, or similar) for wearables or mobile apps
- Background in digital health, biomedical signal processing, or clinical data
- Experience building APIs/data pipelines (FastAPI, cloud data pipelines, etc.) to serve models in production
- Familiarity with regulatory/privacy considerations around health data (HIPAA-style handling, data anonymization)
Full description
Compensation: ₹5,000 – ₹10,000 • No equity
Type: Internship (2–3 months, extendable)
Stipend - 5K - 10KLocation: RemoteAvailability - full time / part time
Make My Health is building health products powered by data from wearables and health platforms — Apple Watch, Google Health/Fit, our own custom devices (e.g. the posture wearable), and other third-party health trackers. We're looking for an AI/ML Engineer to turn that raw sensor and health data into meaningful insights and algorithms — things like activity/posture pattern detection, anomaly or risk scoring, personalized recommendations, and predictive health signals — that power features across our app and devices.
What You'll Do
- Ingest and normalize health data from multiple sources — Apple HealthKit, Google Health Connect/Fit, custom BLE devices, and manually logged data — into a consistent internal data model.
- Design and build ML models/algorithms for health signal detection: e.g. posture/slouch pattern recognition, activity classification, sleep/recovery scoring, anomaly detection, or trend/risk prediction, depending on the product.
- Do exploratory data analysis on time-series sensor data (IMU streams, heart rate, steps, sleep stages, etc.) to identify useful features and signals.
- Prototype and evaluate models (classical ML and/or deep learning where appropriate) and iterate based on accuracy, latency, and battery/compute constraints for on-device or near-real-time use.
- Work with the mobile/firmware teams to figure out what should run on-device (edge inference) vs. in the cloud.
- Turn validated models into production-ready pipelines/APIs that the app and devices can call.
- Continuously evaluate model performance against real user data and retrain/improve as new devices and data sources are added.
- Stay current with relevant health-AI research (wearable sensor fusion, digital biomarkers, etc.) and bring in applicable techniques.
What We're Looking For
- Degree (or equivalent experience) in Computer Science, Data Science, Statistics, Biomedical Engineering, or a related field.
- Strong Python skills and experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn).
- Experience working with time-series or sensor data — wearable/health data experience is a strong plus, but general time-series/IoT/signal-processing experience also counts.
- Comfortable with the full ML lifecycle: data cleaning, feature engineering, model training/evaluation, and deployment.