Senior AI/ML Engineer - Time-Series & Sensor Reasoning

Analog Devices, Inc.; Wilmington (MA); On-site; USD 230,000 - 316,000; Full time

Benefits offered by this job

Medical coverage

Vision coverage

Dental coverage

401k plan

Paid time off

Job summary

Analog Devices, Inc. seeks a Principal AI Engineer in Time-Series & Sensor Foundation Models to advance edge AI by unifying multimodal sensor data into robust time-series reasoning.

You’ll drive architectures that connect electrical, audio, motion, photonic, and physiological signals into context-aware models for real-time inference. You will lead DL research on foundation models, collaborate across hardware and systems teams, and publish at NeurIPS/ICASSP.

Qualifications

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

Responsibilities

• Lead R&D on intelligent time-series agents for edge by combining multiple data modalities.
• Advance sensor fusion and cross-modal alignment across electrical, acoustic, inertial and photonic domains.
• Create benchmarking pipelines for cross-domain time-series foundation models and evaluate robustness.
• Apply alignment and fine-tuning methods such as LoRA, Q-LoRA, adapters for multimodal data.
• Collaborate with hardware and systems teams for real-time, energy-efficient sensing applications.
• Publish at major ML and signal-processing venues and mentor junior researchers.

Skills

Time-series ML

Signal processing

Foundation models

Python

PyTorch

Edge AI

Cross-modal fusion

LoRA/Q-LoRA

Education

Ph.D. in Electrical Engineering, Computer Science, or Applied Physics

Tools

Cloud platforms (AWS, GCP)

Job description

Analog Devices, Inc. seeks a Principal AI Engineer in Time-Series & Sensor Foundation Models to advance edge AI by unifying multimodal sensor data into robust time-series reasoning.

You’ll drive architectures that connect electrical, audio, motion, photonic, and physiological signals into context-aware models for real-time inference. You will lead DL research on foundation models, collaborate across hardware and systems teams, and publish at NeurIPS/ICASSP.