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

Analog Devices•San Jose, CA

10d•Onsite

About The Position

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.

Requirements

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 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.

Nice To Haves

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.

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.
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.