Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models

About the role

Analog Devices is seeking a Principal AI Engineer in Time-Series and Sensor Foundation Models to advance AI at the intersection of sensing, signal intelligence and large-scale temporal modeling. The role develops architectures that unify multimodal sensor data (electrical, audio, motion, photonic and physiological signals) into a foundation for context-aware reasoning across time, contributing to the Faraday suite of physically-intelligent reasoning models. It works on multi-modal time-series reasoning models using state-of-the-art techniques in time-series embeddings, cross-attention, reinforcement learning and time-series agentic solutions.

Key responsibilities

• Lead R&D on intelligent time-series agents for the edge, combining anomaly detection, reasoning and forecasting foundation models that handle electrical, audio, motion, physiological and text data.
• Advance sensor fusion research and create benchmarking pipelines for cross-domain time-series foundation models.
• Apply LoRA, Q-LoRA, adapter tuning and contrastive alignment, and investigate DPO and RLAIF for physical and sensory reasoning.
• Co-design architectures with hardware, signal processing and systems teams for real-time, energy-efficient sensing applications.
• Design statistical experiments to collect sensor data and publish at venues such as NeurIPS, ICLR, ICML, ICASSP and KDD.
• Mentor junior researchers and help shape Lorenz Labs' strategy for foundation models that reason about physical systems.

Required profile

• 10+ years of experience developing software products with an AI/ML focus, and 6+ years developing AI/ML products.
• Deep expertise in time-series ML, signal processing and foundation models (e.g. Chronos, TimesFM, TimeGPT), including hands-on training or fine-tuning and evaluation.
• Ph.D. in Electrical Engineering, Computer Science or Applied Physics preferred; record of innovation through patents, publications or open-source contributions preferred.
• Ability to collaborate across ML, hardware and embedded systems and turn research into deployable systems.
• Willingness to travel about 10% of the time; first shift, days.

Required skills

• Python, PyTorch and large-scale training pipelines on cloud or distributed systems (AWS, GCP).
• Representation learning, time-series encoding, compression and motif discovery.
• Parameter-efficient fine-tuning (LoRA/Q-LoRA) and reward-based optimization (DPO, PPO, RLAIF).
• Statistical hypothesis testing, experimental design and causal discovery.

What we offer

• Expected wage range of $230,000 to $316,250, with a discretionary performance-based bonus.
• Medical, vision and dental coverage, 401k, paid vacation, holidays and sick time.