Real-Time Time-Series AI Engineer (Edge & Cloud)

BrightAI Corporation; Palo Alto (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

BrightAI Corporation seeks an AI Engineer focused on Time-Series Signal Processing to lead real-time AI solutions for multi-modal sensor data. You will prototype and deploy models that operate at the edge and in the cloud, collaborating across hardware, firmware, and software teams.

You will design pipelines for high-frequency data, explore RNN/LSTM and temporal transformer architectures, and apply gradient-boosting models with interpretability techniques.

Qualifications

• Degree in Electrical Engineering, Computer Science, or related field with focus on signal processing and ML.
• Solid track record in time-series modeling or real-time AI systems.

Responsibilities

• Design real-time signal processing and ML pipelines for multi-modal time-series data.
• Develop ML models for classification, prediction, anomaly detection, and pattern analysis.
• Lead research on RNNs/LSTMs and temporal transformers.
• Build and tune tree-based models for time-series tasks with feature engineering.
• Work with SCADA and industrial telemetry data, ingesting multi-channel streams.
• Collaborate with hardware and product teams to deploy models on edge devices.
• Optimize signal-processing techniques to improve model input quality.
• Maintain scalable workflows for data labeling, training, and evaluation.
• Stay current with time-series modeling advances and integrate into roadmaps.
• Ensure robustness and reliability in production, including edge deployments.

Skills

Time-series modeling

Signal processing

Python

RNNs/LSTMs

Temporal transformers

XGBoost

LightGBM

Edge deployment

Data labeling

Collaboration

Education

Bachelor's degree in Electrical Engineering, CS or related

Strong background in signal processing / time-series

Tools

PyTorch

TensorFlow

Keras

SHAP

MLflow

DVC

ONNX

TFLite

CI/CD

Git

JIRA

Job description

BrightAI Corporation seeks an AI Engineer focused on Time-Series Signal Processing to lead real-time AI solutions for multi-modal sensor data. You will prototype and deploy models that operate at the edge and in the cloud, collaborating across hardware, firmware, and software teams.

You will design pipelines for high-frequency data, explore RNN/LSTM and temporal transformer architectures, and apply gradient-boosting models with interpretability techniques.