AI Engineer

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. This is a critical role focused on modeling and understanding time-series signals coming from IoT devices equipped with various sensors (IMU, acoustic, pressure, temperature, etc.) 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.
• Build and tune classical and tree-based ML models (XGBoost, LightGBM, Random Forests, and other gradient-boosted ensembles) for time-series tasks, including feature engineering and model interpretability (e.g., 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.
• Drive experimentation and optimization of signal-processing techniques (e.g., 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