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

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

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 Staff 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.
• Leveare 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

• 6+ 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 dimensional temporal data.
• Knowledge of SOTA models in time series reasoning (based on cross-attention and multi-modal embedding), time series agentic systems, time series memory and RAG.
• Parameter-efficient fine-tuning, LoRA/Q-LoRA, and reward-based optimization methods (DPO, PPO, RLAIF).
• Strong knowledge in statistical hypothesis testing, experimental design, causal discovery.
• 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.

Preferred Education and Experience

• Ph.D. in Electrical Engineering, Computer Science, or Applied Physics.
• Demonstrated leadership and agility in combining technical solutions to business problems, preferably for embedded systems.
• Record of innovation through patents, publications, or open-source contributions.

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Dep