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BrightAI · 3 months ago

Senior AI Engineer, Time-Series Signal Processing

Palo Alto, CA

Full-time

Onsite

Senior Level

5+ years exp

Bright.AI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. They are seeking a Senior AI Engineer – Time-Series Signal Processing to lead the development of AI/ML solutions built on high-frequency multi-modal sensor data, focusing on modeling and understanding time-series signals from IoT devices.

Artificial Intelligence (AI)Cloud ComputingInternet of ThingsMobile

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

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

Qualification

Time-series modelingDigital signal processingMachine learning solutionsPython programmingRNNsLSTMsReal-time systemsData labeling practicesCollaboration skillsCommunication skills

Required

M.S. or Ph.D. in Electrical Engineering, Computer Science, or a related field, with a strong focus on signal processing, time-series analysis, and machine learning

5+ 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

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

Preferred

Experience building end-to-end AI systems for structural health monitoring, condition monitoring, anomaly detection, activity recognition, or motion tracking

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