AI Engineer, Time-Series Signal Processing

BrightAI Corporation•Palo Alto, CA

About The Position

BrightAI is hiring an AI Engineer focused on time-series signal processing to develop AI/ML solutions using high-frequency, multimodal sensor data. The role involves modeling signals from IoT devices equipped with sensors such as IMUs, microphones, pressure, temperature, and ultrasonic sensors. These solutions support intelligent automation across physical infrastructure systems. The engineer will build real-time AI models that process noisy, high-throughput data streams and produce insights for decision-making at both the edge and cloud scale. The position spans signal processing, time-series machine learning, industrial telemetry, scalable data workflows, and production deployment, with collaboration across hardware, embedded, and product teams.

Requirements

At least 2 years of experience developing signal-processing and machine-learning solutions for time-series sensor data.
Track record of bringing at least one machine-learning solution to market.
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.
Deep understanding of digital signal-processing methods, including filtering, sampling, windowing, FFT, and feature extraction.
Hands-on experience with RNNs, especially LSTMs or GRUs, and/or temporal convolutional networks for time-series modeling.
Proficiency with tree-based and gradient-boosting models such as XGBoost, LightGBM, and Random Forests applied to time-series and sensor data, including hyperparameter tuning and explainability.
Experience working with SCADA systems and industrial telemetry data, including high-frequency sensor feeds, time-stamped operational data, and multichannel ingestion from physical assets.
Proven experience with time-series data from physical sensors such as IMUs, microphones, vibration sensors, or pressure sensors.
Strong Python coding skills and fluency with machine-learning and deep-learning frameworks such as PyTorch, TensorFlow, or Keras.
Experience optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.
Fluency with best practices for time-series data labeling, augmentation, and evaluation.
Excellent problem-solving and collaboration skills, including the ability to work across teams.
Strong communication skills and ability to convey findings and recommendations to internal and external stakeholders.

Nice To Haves

Experience building end-to-end AI systems for structural health monitoring, condition monitoring, anomaly detection, activity recognition, or motion tracking.
Experience with predictive maintenance on industrial equipment using SCADA or telemetry data.
Familiarity with experiment-tracking and model-lifecycle tools such as MLflow or DVC.
Exposure to streaming or online inference patterns, such as EWMA normalization and windowed feature extraction on live data.
Proficiency in embedded software or deploying models to constrained environments using tools such as TFLite, ONNX, or custom firmware.
Familiarity with containerized workflows and Linux-based development environments.
Experience with Agile workflows and tools such as JIRA, Git, and CI/CD pipelines.
Prior work in startup or high-pace teams, including experience building real-time systems from the ground up.

Responsibilities

Design and implement real-time signal-processing and machine-learning pipelines for multimodal time-series data from sensors such as IMUs, microphones, pressure or force sensors, and ultrasonic transducers.
Develop and deploy models for time-series classification, prediction, anomaly detection, activity recognition, condition monitoring, and pattern analysis.
Lead research and implementation of RNN architectures, especially LSTMs and variants, and temporal transformer models as needed.
Build and tune classical and tree-based machine-learning models, including XGBoost, LightGBM, and Random Forests, with feature engineering and interpretability methods such as SHAP.
Work with SCADA systems and industrial telemetry, ingesting and modeling high-frequency, multichannel operational data from physical assets.
Collaborate with hardware, embedded, and product teams to integrate models into edge devices and IoT platforms.
Experiment with and optimize signal-processing techniques such as filtering, feature extraction, and event detection to improve model inputs.
Design and maintain scalable workflows for ingesting, labeling, training, and evaluating multichannel time-series datasets.
Stay current with advances in time-series modeling, signal processing, and real-time inference, and incorporate relevant advances into product roadmaps.
Ensure model robustness, performance, and reliability in production, including edge deployments.