Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs)

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers.

With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com  and on LinkedIn and X . About the Role  We are   seeking   a Principal AI   Engineer in Time-Series & Sensor Foundation Models to advance AI engineering at the intersection of sensing, signal intelligence, and large-scale temporal modeling.

This role will develop architectures that unify multimodal sensor data—including electrical, audio, motion, photonic, and physiological signals—into a coherent foundation for context-aware reasoning across time. Your work will contribute directly to ADI’s Faraday suite of   physically-intelligent   reasoning models. Building on ADI’s leadership in sensing and edge intelligence, you will extend foundation-scale modeling into domains such as automotive, health, industrial systems, and robotics—enabling time series feature extraction, anomaly detection, forecasting, and cross-sensor understanding that bridge physics and AI.

You will be working on multi-modal time series reasoning models which will be capable of reasoning about sensor signals,   utilizing   state-of-the-art   techniques in time series embeddings, cross-attention, reinforcement   learning   and time series agentic solutions. Key Responsibilities   Lead R&D on creation of intelligent time-series agents for edge by combining time series anomaly detection, reasoning, forecasting foundation   models; these   models will be able to incorporate multiple data modalities such as electrical, audio, motion, physiological as well as text.

Besides the time series   modality   these models will be able to use other modalities such as text and image, which will serve as   additional   context. Advance research in sensor fusion, enabling cross-modal alignment between electrical, acoustic, inertial, and photonic domains. Create benchmarking pipelines for cross-domain time-series foundation models, covering   representation   robustness, interpretability, and hardware performance metrics.

Apply alignment and fine-tuning methods such as   LoRA, Q-LoRA, adapter-tuning, and contrastive alignment for multimodal sensor datasets. Leverage SOTA research in time series embedding and compression to enable time series reasoning models for edge,  Investigate modern foundation alignment techniques, including DPO (Direct Preference Optimization) and RLAIF (Reinforcement Learning from AI Feedback) for physical and sensory reasoning tasks.

Partner with ADI’s hardware, signal processing, and systems teams to co-design architectures for real-time, energy-efficient sensing applications. Work on   design   of statistical experiments for SMEs to collect sensor data for model development. Publish and represent ADI at major ML and signal-processing venues (NeurIPS, ICLR, ICML, ICASSP, KDD), often in conjunction with leading AI industry partners.

Mentor junior researchers and help shape Lorenz Labs’ strategy for foundation models that understand and reason   about   physical systems. Must Have Skills 10+ years of experience developing AI/ML products Deep   expertise   in time-series ML, signal processing, and foundation models (Chronos,   TimesFM ,   TimeGPT, etc.) – understanding of tradeoffs of different architectures, hands on experience of training or fine-tuning one or more of the time series foundation models, evaluation of different models.

Proficiency   in representation learning, time series encoding, time series   compression   and motif discovery in high