AI Engineer, Time-Series Signal Processing
Brightai · Palo Alto, CA
About this role
**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’re 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 role focuses on modeling and understanding time-series signals from **IoT devices** equipped with sensors such as **IMU, acoustic, pressure, temperature**, and more—driving intelligent automation across physical infrastructure systems. You’ll build **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 (e.g., IMUs, microphones, pressure/force sensors, ultrasonic transducers). - 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 variants) and **temporal transformer models** as needed. - Build and tune **classical and tree-based ML models** (e.g., **XGBoost, LightGBM, Random Forests**) including **feature engineering** and **model interpretability** (e.g., **SHAP**). - Work with **SCADA systems** and industrial telemetry data to ingest and model **high-frequency, multi-channel** operational streams. - Collaborate with **hardware, embedded, and product teams** to integrate models into **edge devices** and **IoT platforms**. - Drive experimentation and optimization of signal-processing techniques (filtering, feature extraction, event detection) to improve 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 related**, with 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** building 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)**: filtering, sampling, windowing, FFT, feature extraction, etc. - Hands-on experience with **RNNs** (especially **LSTMs/GRUs**) and/or **temporal convolutional networks**. - Proficiency with **tree-based and gradient-boosting models** (XGBoost, LightGBM, Random Forests) for time-series/sensor tasks, including hyperparameter tuning and explainability. - Experience with **SCADA systems** and industrial telemetry data (high-frequency feeds, time-stamped ope
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