Senior AI Engineer, Time-Series Signal Processing
Core
Design and implement real-time AI/ML pipelines for high-frequency multi-modal sensor data (IMU, acoustic, pressure, temperature) to drive intelligent automation in physical infrastructure systems.
Role type
Senior IC machine-learning engineer (time-series signal processing)
Builds
Real-time AI models for classification, prediction, anomaly detection, and condition monitoring deployed at edge and cloud scale.
Domain
Industrial IoT, Physical AI, Time-Series Signal Processing
Deliverable
production ML models
Required skills
Digital signal processing (DSP), RNNs (LSTMs/GRUs), temporal transformers, tree-based ML (XGBoost, LightGBM), Python, PyTorch/TensorFlow, edge deployment, feature engineering, model interpretability (SHAP)
Preferred skills
Structural health monitoring, predictive maintenance, experiment tracking (MLflow/DVC), streaming inference, embedded software, containerized workflows
Technologies
PyTorch, TensorFlow, Keras, XGBoost, LightGBM, Random Forests, SHAP, MLflow, DVC, TFLite, ONNX, JIRA, Git
Responsibilities
Design real-time signal processing and ML pipelines for multi-modal time-series data; Develop and deploy ML models for time-series classification, prediction, anomaly detection, and pattern analysis; Lead research and implementation of RNN-based and temporal transformer architectures; Build and tune classical and tree-based ML models for time-series tasks; Work with SCADA systems and industrial telemetry data; Collaborate with hardware and embedded teams to integrate models into edge devices; Drive experimentation and optimization of signal-processing techniques; Design scalable workflows for data ingestion, labeling, training, and evaluation; Ensure model robustness and reliability in production environments.
Seniority
Senior, hands-on IC