Job description

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 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. Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras). Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems. Fluency with best practices in data labeling, augmentation, and evaluation for time-series tasks. Excellent problem-solving and collaboration skills with the ability to work across teams. Strong communication skills with the ability to convey findings and recommendations to internal and external stakeholders. Bonus Qualifications 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/telemetry data. Familiarity with experiment tracking and model lifecycle tooling (e.g., MLflow, DVC). Exposure to streaming/online inference patterns (e.g., EWMA normalization, windowed feature extraction on live data). Proficiency in embedded software or deploying models to constrained environments (e.g., using 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 with experience in building real-time systems from the ground up.

Requirements

Must-haves

AI Engineer, Time-Series Signal Processing
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.
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
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.
Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras).
Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.

Nice-to-haves

Bonus Qualifications

Application prep

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Keywords to mirror

aimlmachine learningpythonagilejiraci/cd

Platform: greenhouse
Effort: quick