Machine Learning Engineer

• 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.