AI Engineer, Time-Series Signal Processing

Required

• 2+ years of experience developing signal processing and ML solutions for time-series sensor data. Track record of bringing at least one ML solution to market.
• Deep understanding of digital signal processing (DSP) methods: filtering, sampling, windowing, FFT, feature extraction, etc.
• Hands-on experience with RNNs (especially LSTMs/GRUs) and/or temporal convolutional networks for time-series modeling.
• Proficiency with tree-based and gradient-boosting models (XGBoost, LightGBM, Random Forests) applied to time-series and sensor data, including hyperparameter tuning and explainability.
• Experience working with SCADA systems and industrial telemetry data (high-frequency sensor feeds, time-stamped operational data, multi-channel ingestion from physical assets).
• Proven experience with time-series data from physical sensors such as IMUs, microphones, vibration or pressure sensors.
• Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras).
• Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.
• Fluency with best practices in data labeling, augmentation, and evaluation for time-series tasks.
• Excellent problem-solving and collaboration skills with the ability to work across teams.
• Strong communication skills with the ability to convey findings and recommendations to internal and external stakeholders.
• Bonus Qualifications