Staff AI Engineer: Time-Series & Multimodal Edge

1010 Analog Devices Inc.; San Jose (CA); On-site; USD 198,000 - 272,000; Full time

Benefits offered by this job

Medical vision dental coverage

401k

Paid vacation and holidays

Discretionary bonus

Job summary

Analog Devices, Inc. in the United States seeks a Staff AI Engineer focused on Time-Series & Sensor Foundation Models to advance AI engineering at the intersection of sensing and temporal modeling.

This role develops architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time. Working on ADI’s Faraday suite, you will extend foundation-scale modeling into automotive, health, industrial systems, and robotics—enabling time series feature

Qualifications

• 6+ years building AI/ML products and systems.
• Expertise in time-series modeling and signal processing.
• Experience with foundation models and large-scale training.
• Proficiency in Python and PyTorch; cloud pipelines.
• Ability to collaborate across ML, hardware, and systems.

Responsibilities

• Lead R&D on intelligent time-series agents for edge with multimodal data.
• Advance cross-modal sensor fusion and benchmarking pipelines.
• Apply LoRA/Q-LoRA and other fine-tuning methods for multimodal data.
• Publish at major ML and signal-processing venues.
• Mentor researchers and shape strategy for foundation models.

Skills

Time-series ML

Signal processing

Foundation models

Python

PyTorch

Cloud training

Cross-disciplinary collaboration

Education

Ph.D. in Electrical Engineering or CS or Applied Physics

Tools

AWS

GCP

PyTorch

Job description

Analog Devices, Inc. in the United States seeks a Staff AI Engineer focused on Time-Series & Sensor Foundation Models to advance AI engineering at the intersection of sensing and temporal modeling.

This role develops architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time. Working on ADI’s Faraday suite, you will extend foundation-scale modeling into automotive, health, industrial systems, and robotics—enabling time series feature