BrightAI builds physical AI systems and an end-to-end Stateful platform that ingests sensor, drone, and wearable data to monitor and predict failures in infrastructure assets across multiple verticals.
<p><strong>AI Engineer, Time-Series Signal Processing</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>Responsibilities</strong></p> <ul> <li>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.</li> <li>Develop and deploy ML models for time-series classification, prediction, anomaly detection, activity recognition, condition monitoring and pattern analysis.</li> <li>Lead research and implementation of RNN-based architectures (especially LSTMs and their variants) as well as temporal transformer models as needed.</li> <li>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).</li> <li>Work with SCADA systems and industrial telemetry data—ingesting and modeling high-frequency, multi-channel operational data streams from physical assets.</li> <li>Collaborate with hardware, embedded, and product teams to integrate models into edge devices and IoT platforms.</li> <li>Drive experimentation and optimization of signal-processing techniques (e.g., filtering, feature extraction, event detection) to enhance model input quality.</li> <li>Design and maintain scalable workflows for ingesting, labeling, training, and evaluating multi-channel time-series datasets.</li> <li>Stay current with advances in time-series modeling, signal processing, and real-time inference, and incorporate them into product roadmaps.</li> <li>Ensure model robustness, performance, and reliability in production environments, including edge deployments.</li> </ul> <p><strong>Educational Background</strong></p> <ul> <li>Degree in Electrical Engineering, Computer Science, or a related field, with a strong focus on signal processing, time-series analysis, and machine learning.</li> <li>Strong academic or industry track record in time-series modeling, signal processing, or real-time AI systems.</li> </ul> <p><strong>Required Skills & Expertise</strong></p> <ul> <li>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.</li> <li>Deep understanding of digital signal processing (DSP) methods: filtering, sampling, windowing, FFT, feature extraction, etc.</li> <li>Hands-on experience with RNNs (especially LSTMs/GRUs) and/or temporal convolutional networks for time-series modeling.</li> <li>Proficiency with tree-based and gradient-boosting models (XGBoost, LightGBM, Random Forests) applied to time-series and sensor data, including hyperparameter tuning and explainability.</li> <li>Experience working with SCADA systems and industrial telemetry data (high-frequency sensor feeds, time-stamped operational data, multi-channel ingestion from physical assets).</li> <li>Proven experience with time-series data from physical sensors such as IMUs, microphones, vibration or pressure sensors.</li> <li>Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras).</li> <li>Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.</li> <li>Fluency with best practices in data labeling, augmentation, and evaluation for time-series tasks.</li> <li>Excellent problem-solving and collaboration skills with the ability to work across teams.</li> <li>Strong communication skills with the ability to convey findings and recommendations to internal and external stakeholders.</li> </ul> <p><strong> Bonus Qualifications</strong></p> <ul> <li>Experience building end-to-end AI systems for structural health monitoring, condition monitoring, anomaly detection, activity recognition, or motion tracking.</li> <li>Experience with predictive maintenance on industrial equipment using SCADA/telemetry data.</li> <li>Familiarity with experiment tracking and model lifecycle tooling (e.g., MLflow, DVC).</li> <li>Exposure to streaming/online inference patterns (e.g., EWMA normalization, windowed feature extraction on live data).</li> <li>Proficiency in embedded software or deploying models to constrained environments (e.g., using TFLite, ONNX, or custom firmware).</li> <li>Familiarity with containerized workflows and Linux-based development environments.</li> <li>Experience with Agile workflows and tools such as JIRA, Git, and CI/CD pipelines.</li> <li>Prior work in startup or high-pace teams with experience in building real-time systems from the ground up.</li> </ul>