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BrightAI Corporation

AI Engineer, Time-Series Signal Processing

Palo Alto, CAmidAdded today

About this role

BrightAI seeks an AI Engineer to develop real-time machine learning models for high-frequency multi-modal sensor data from IoT devices and industrial systems. You'll design signal processing pipelines, build time-series classification and anomaly detection models, and optimize them for edge and cloud deployment across physical infrastructure automation.

What you'll do

  • Design and implement real-time signal processing pipelines for multi-modal time-series data from sensors like IMUs, microphones, and pressure transducers
  • Develop ML models for time-series classification, anomaly detection, activity recognition, and predictive maintenance using RNNs and temporal transformers
  • Build and tune tree-based models (XGBoost, LightGBM) with feature engineering and SHAP-based interpretability for sensor data
  • Work with SCADA and industrial telemetry systems to ingest and model high-frequency multi-channel operational data streams
  • Optimize signal-processing techniques including filtering, feature extraction, and event detection to improve model inputs
  • Collaborate with hardware and product teams to integrate models into edge devices and ensure production robustness

What they're looking for

  • Time-series modeling and signal processing (DSP, filtering, FFT, windowing)
  • RNNs/LSTMs, temporal convolutional networks, and transformer architectures
  • Tree-based and gradient-boosting models (XGBoost, LightGBM, Random Forests)
  • Python and ML frameworks (PyTorch, TensorFlow, Keras)
  • SCADA systems and industrial telemetry data handling
  • Edge device deployment and model optimization for resource-constrained environments
  • Data labeling, augmentation, and evaluation best practices
  • Cross-functional collaboration and technical communication
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BrightAI Corporation

BrightAI Corporation develops computer vision and perception systems that leverage modern deep learning techniques. The company is hiring for graduate-level roles and engineering positions focused on building and training production computer vision models, from dataset preparation through model deployment.

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Likely interview questions

  • Walk us through a time-series ML solution you brought to production—what were the key signal processing steps and how did you handle noisy sensor data?
  • How have you approached feature engineering for high-frequency multi-modal sensor data, and what DSP techniques did you find most effective?
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