Principal Time-Series & Sensor AI Engineer

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

Job summary

Analog Devices, Inc. seeks a Principal AI/ML Engineer focusing on time-series and sensor reasoning to unify multimodal data into robust foundation models for edge deployment.

You will drive research in cross-modal fusion, time-series embeddings, and alignment techniques, collaborating with hardware and embedded teams to deliver energy-efficient sensing solutions. Expect leadership responsibilities and contributions to Lorenz Labs’ strategic roadmap.

Qualifications

• 10+ years of AI/ML product development experience.
• Deep expertise in time-series ML, signal processing, and foundation models.
• Proficiency in representation learning, time-series encoding, and compression.
• Knowledge of SOTA time-series reasoning models and cross-attention.
• Fine-tuning methods like LoRA/Q-LoRA; reward-based optimization (DPO, PPO, RLAIF).
• Strong statistics, experimental design, and causal discovery.

Responsibilities

• Lead R&D on intelligent time-series agents for edge with multimodal data.
• Advance sensor fusion and cross-modal alignment across modalities.
• Create benchmarking pipelines for cross-domain time-series models.
• Apply alignment techniques (LoRA, LoRA variants, DPO) for multimodal datasets.
• Collaborate with hardware, signal processing, and embedded teams to deploy real-time sensing systems.
• Publish and represent Lorenz Labs at major ML and signal-processing venues.
• Mentor junior researchers and shape Lorenz Labs’ foundation-model strategy.

Skills

AI/ML products

Time-series ML

Representation learning

Time series reasoning

LoRA / Q-LoRA

DPO / RLAIF

Statistics & experimental design

Python / PyTorch / cloud pipelines

Education

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

Tools

AWS

GCP

Job description

Analog Devices, Inc. seeks a Principal AI/ML Engineer focusing on time-series and sensor reasoning to unify multimodal data into robust foundation models for edge deployment.

You will drive research in cross-modal fusion, time-series embeddings, and alignment techniques, collaborating with hardware and embedded teams to deliver energy-efficient sensing solutions. Expect leadership responsibilities and contributions to Lorenz Labs’ strategic roadmap.