About the role
- Build, train, and deploy efficient ML models and inference pipelines for real-world Android
devices. Focus on time-series sensor intelligence, lightweight classifiers, and scalable
backend learning systems.
Key Responsibilities - Design, train, and optimize small ML classifiers (XGBoost, RandomForest, SVM,
MLP, 1D-CNN, GRU/LSTM when sequence context is required).
- Build feature extraction pipelines for high-frequency sensor data (100 Hz class
problems, 1-sec to multi-sec windows).
- Ensure models are device-agnostic, orientation-invariant, and robust to sensor noise
across a diverse Android hardware mix.
- Implement real-time inference pipelines that run efficiently on CPU-constrained
mobile environments using Android NDK or TFLite-compatible formats.
- Build backend learning services that can handle thousands of concurrent sessions
and continuous model improvement from field data. Own model validation, performance benchmarking, drift analysis, false-positive
reduction, and temporal smoothing.
- Collaborate closely with native C++ and DevOps teams for clean deployment and
monitoring.
Requirements
- Strong command of ML for time-series and sensor data.
- Hands-on experience with lightweight models that run on Android devices.
- Expertise in XGBoost or tree-based classifiers + feature engineering.
- Experience converting models to TFLite, ONNX, or similar mobile inference formats.
- Deep understanding of normalization, interpolation, filtering, debouncing,
HMM/majority voting, and classifier calibration.
- Solid grounding in model evaluation metrics and edge-case handling.
- Experience with sensor fusion pipelines, movement feature intelligence, or inertial
embeddings.
- Deployment experience on Kubernetes + cloud monitoring.