Staff AI Engineer, Time-Series & Edge Foundation Models

Analog Devices; San Jose (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Analog Devices, Inc. in California seeks a Staff AI Engineer for Time-Series & Sensor Foundation Models to advance edge AI. Build architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time, contributing to the Faraday suite of models.

You will lead R&D on time-series agents for edge devices, enable cross-modal data fusion, and publish at top ML venues. Collaboration with hardware teams is essential for energy-efficient sensing solutions.

Qualifications

• 6+ years of experience developing AI/ML products.
• Deep expertise in time-series ML, signal processing, and foundation models.
• Proficiency in representation learning, time series encoding, and time series compression.
• Knowledge of state-of-the-art time series reasoning models and cross-attention.
• Experience with LoRA/Q-LoRA and reinforcement learning methods.
• Strong statistical hypothesis testing, experimental design, and causal discovery.
• Fluency in Python, PyTorch, and large-scale training pipelines on cloud platforms.
• Ability to collaborate across ML, hardware, and embedded systems.

Responsibilities

• Lead R&D on intelligent time-series agents for edge with multimodal data.
• Integrate modalities like text and image for contextual reasoning.
• Advance sensor fusion and cross-modal alignment across domains.
• Create benchmarking pipelines for cross-domain time-series models.
• Apply alignment and fine-tuning methods (LoRA, Q-LoRA, adapters).
• Pursue SOTA in time-series embeddings and compression for edge use.
• Explore DPO and RLAIF for physical/sensory reasoning tasks.
• Collaborate with hardware, signal processing, and systems teams.
• Design statistical experiments to collect sensor data for model development.
• Publish at NeurIPS, ICML, ICLR and related venues; mentor junior researchers.

Skills

AI/ML products

Time-series ML

Foundation models

Representation learning

Time series encoding

Time series compression

Cross-modal embedding

LoRA fine-tuning

DPO/RLAIF methods

Statistical testing

Python

PyTorch

Cloud pipelines

Distributed systems

Education

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

Tools

PyTorch

AWS

GCP

Distributed training

Job description

Analog Devices, Inc. in California seeks a Staff AI Engineer for Time-Series & Sensor Foundation Models to advance edge AI. Build architectures unifying multimodal sensor data into a coherent foundation for context-aware reasoning across time, contributing to the Faraday suite of models.

You will lead R&D on time-series agents for edge devices, enable cross-modal data fusion, and publish at top ML venues. Collaboration with hardware teams is essential for energy-efficient sensing solutions.