Principal Time-Series & Sensor AI Architect

1010 Analog Devices Inc.; San Jose (CA); On-site; USD 230,000 - 316,000; Full time

Benefits offered by this job

Discretionary bonus

Comprehensive benefits

Job summary

Analog Devices, Inc. seeks a Principal AI Engineer in Time-Series & Sensor Foundation Models to advance AI engineering at the intersection of sensing, signal intelligence, and large-scale temporal modeling.

You will build architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time. Your work enables time series feature extraction, anomaly detection, forecasting, and cross-sensor understanding bridging physics and AI.

Qualifications

• 10+ years of experience developing AI/ML products.
• Deep expertise in time-series ML, signal processing, and foundation models.
• Fluency in Python and PyTorch; experience with large-scale training pipelines.

Responsibilities

• Lead R&D on intelligent time-series agents for edge by combining anomaly detection, reasoning, and foundation models.
• Advance research in sensor fusion and cross-modal alignment across multiple data modalities.
• Publish and represent ADI at major ML and signal-processing venues.
• Mentor junior researchers and help shape Lorenz Labs’ strategy for foundation models.

Skills

Time-series ML

Signal processing

Foundation models

Python

PyTorch

Cloud pipelines

Cross-modal learning

LoRA/Q-LoRA

RLAIF/DPO

Experiment design

Time-series embeddings

Education

Ph.D. in EE/CS/Applied Physics

Tools

AWS

GCP

Distributed training

Job description

Analog Devices, Inc. seeks a Principal AI Engineer in Time-Series & Sensor Foundation Models to advance AI engineering at the intersection of sensing, signal intelligence, and large-scale temporal modeling.

You will build architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time. Your work enables time series feature extraction, anomaly detection, forecasting, and cross-sensor understanding bridging physics and AI.