Sr. AI Engineer, Time-Series Signal Processing | BrightAI · Teeming.ai
BrightAI
BrightAI is a hyper-growth Infrastructure AIoT copilot platform company with a mission to revitalize and automate western infrastructure. Our commitment lies in empowering major infrastructure services companies through cutting-edge solutions encompassing autonomous monitoring, inspection, and close-loop control. These transformative tools are designed for universal retrofitting to legacy infrastructure, seamlessly augmented with frontline AI copilot capabilities. By leveraging BrightAI, our customers seamlessly deploy cost-effective AI endpoints numbering in the hundreds of thousands. We pride ourselves on being the trusted force behind our customers' frontline services, fostering proactivity, and driving down costs.
BrightAI is a hyper-growth Infrastructure AIoT copilot platform company with a mission to revitalize and automate western infrastructure. Our commitment lies in empowering major infrastructure services companies through cutting-edge solutions encompassing autonomous monitoring, inspection, and close-loop control. These transformative tools are designed for universal retrofitting to legacy infrastructure, seamlessly augmented with frontline AI copilot capabilities. By leveraging BrightAI, our customers seamlessly deploy cost-effective AI endpoints numbering in the hundreds of thousands. We pride ourselves on being the trusted force behind our customers' frontline services, fostering proactivity, and driving down costs.
Product: Stateful OS — edge + multimodal AI platform for infrastructure monitoring and autonomous operations
Scale: >250,000 AI endpoints across 25,000+ sites (reported)
Revenue milestone: $80M reported while bootstrapped
Funding: $15M seed (Nov 2024); $51M Series A (Jul 2025) reported
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Company Overview
Problem Domain
Infrastructure monitoring, inspection, and autonomous operations for critical physical systems
Founded
2019
Industry
Software Development
Funding Track Record
Seed- Nov 2024
$15M
Raised after bootstrapping to reported revenue
Series A- Jul 18, 2025
$51M
Reported participation from BoxGroup, Marlinspike, VSC Ventures, Rsquared VC, Cooley LLP, and others
Investor Signal
“Backed by prominent venture firms including Upfront Ventures (seed) and Khosla Ventures & Inspired Capital (Series A)”
Founders
What we do
Join the Team
Sr. AI Engineer, Time-Series Signal Processing
On-SitePalo Alto, US
On-Site • Palo Alto, US
Bright.AI
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 a
Senior 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.
Educational Background
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.
Strong academic or industry track record in time-series modeling, signal processing, or real-time AI systems.
Required Skills & Expertise
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.
Bonus Qualifications
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.
Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.
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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.
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.