Principal Engineer – Time-Series & Sensor Reasoning Models (Lorenz Labs)

Analog Devices•San Jose, CA

21d•$170,775 - $256,163

About The Position

Analog Devices (NASDAQ: ADI) is a global leader in semiconductors that bridge the physical and digital worlds. Our mission is to enable breakthroughs at the Intelligent Edge—where sensors, compute , and AI converge to transform industries from mobility to healthcare. Lorenz Labs, ADI’s advanced AI engineering group within Edge AI, is pioneering the frontier of Physical Intelligence—developing foundation models and agentic sys tems that can reason about the physical world. We are building the next generation of models that go beyond language and vision, into time, signals, and embodied experience. Our long-term ambition is the realization of an Artificial Engineer: an AI capable of understanding, simulating, and designing electro-physical systems with human-like intuition—complemented by the development of highly optimized embedded models for Edge AI. We are seeking a Principal 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 audio, motion, photonic, and physiological signals—into a coherent foundation for context-aware reasoning across time. Your work will contribute directly to ADI’s PhysGPT 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 health, industrial systems, and robotics—enabling anomaly detection, forecasting, and cross-sensor understanding that bridge physics and AI. You will explore compact architectures such as Tiny Recursive Models and other efficient recurrent paradigms for resource-constrained edge inference, while advancing contextually-aware audio reasoning and sensor fusion learning frameworks that enable systems to interpret their environment with human-like sensitivity. Beyond runtime intelligence, your work will extend into design-time reasoning—developing models and tools that accelerate the creation and optimization of foundation models through physics alignment and tool-in-the-loop optimization, transforming how AI learns from and designs for the physical world.

Requirements

Deep expertise in time-series ML, signal processing, and foundation models (Chronos, TimesFM , TimeGPT , etc.).
Strong background in sensor modeling and signal fusion (e.g., PPG, IMU, audio, photonics, or industrial sensors).
Experience in context-aware and multimodal reasoning—especially involving audio perception , biosignals , or environmental context.
Proficiency in representation learning, causal inference, and motif discovery in high-dimensional temporal data.
Familiarity with benchmarking, evaluation, and robustness testing of foundation and fine-tuned models.
Proven hands-on expertise with modern alignment and fine-tuning strategies, including parameter-efficient fine-tuning, LoRA /Q- LoRA , and reward-based optimization methods (DPO, PPO, RLAIF).
Fluency in Python, PyTorch , and large-scale training pipelines using cloud or distributed systems (AWS, GCP, etc.).
Ability to collaborate across disciplines—ML, hardware, and embedded systems—and translate research into deployable physical intelligence systems.

Nice To Haves

Ph.D. in Electrical Engineering, Computer Science, or Applied Physics.
10+ years of combined research and industrial experience in ML, signal processing, or embedded sensing.
Demonstrated leadership in bridging sensing hardware with foundation model architectures.
Record of innovation through patents, publications, or open-source contributions.

Responsibilities

Lead R&D on time-series foundation models that integrate multi-sensor streams (e.g., audio, motion, environmental, and physiological).
Develop compact, recursive, and hybrid modeling approaches (e.g., Tiny Recursive Models, Liquid Neural Networks, State-Space Transformers) for efficient deployment on edge hardware.
Advance research in sensor fusion, enabling cross-modal alignment between acoustic, inertial, and photonic domains.
Explore audio reasoning models that interpret context and intent through dynamic acoustic and environmental cues.
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.
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.
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.