AI Engineer, Time-Series Signal Processing BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our platform processes visual, spatial, and temporal data from billions of real-world events—captured through edge devices, mobile sensors, and large-scale cloud infrastructure—to deliver intelligent, real-time decisions. We are now hiring an AI Engineer – Time-Series Signal Processing to lead the development of AI/ML solutions built on high-frequency multi-modal sensor data.

) that drive intelligent automation across physical infrastructure systems. You'll work on building cutting-edge real-time AI models that process noisy, high-throughput data streams and extract meaningful insights for real-world decision-making—at both the edge and cloud scale.

Responsibilities

Design and implement real-time signal processing and ML pipelines for multi-modal time-series data such as those acquired from IMUs, microphones, pressure or force sensors, ultrasonic transducers, and similar sensor sources. Develop and deploy ML models for time-series classification, prediction, anomaly detection, activity recognition, condition monitoring and pattern analysis. Lead research and implementation of RNN-based architectures (especially LSTMs and their variants) as well as temporal transformer models as needed.

, SHAP). Work with SCADA systems and industrial telemetry data—ingesting and modeling high-frequency, multi-channel operational data streams from physical assets. Collaborate with hardware, embedded, and product teams to integrate models into edge devices and IoT platforms.

, filtering, feature extraction, event detection) to enhance model input quality. Design and maintain scalable workflows for ingesting, labeling, training, and evaluating multi-channel time-series datasets. Stay current with advances in time-series modeling, signal processing, and real-time inference, and incorporate them into product roadmaps.

Ensure model robustness, performance, and reliability in production environments, including edge deployments. Educational Background Degree in Electrical Engineering, Computer Science, or a related field, with a strong focus on signal processing, time-series analysis, and machine learning. Strong academic or industry track record in time-series modeling, signal processing, or real-time AI systems.

Required Skills Expertise 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. , PyTorch, TensorFlow, Keras).