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

We areseekinga Principal AIEngineer 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 ofphysically-intelligentreasoning 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,utilizingstate-of-the-arttechniques in time series embeddings, cross-attention, reinforcementlearningand 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 foundationmodels; thesemodels will be able to incorporate multiple data modalities such as electrical, audio, motion, physiological as well as text. 

• Besides the time seriesmodalitythese models will be able to use other modalities such as text and image, which will serve asadditionalcontext. 

• 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, coveringrepresentationrobustness, interpretability, and hardware performance metrics. 

• Apply alignment and fine-tuning methods such asLoRA, 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 ondesignof 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 reasonaboutphysical systems. 

Must Have Skills

• Deepexpertisein 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. 

• Proficiencyin representation learning, time series encoding, time seriescompressionand motif discovery in high dimensional temporal data. 

• Knowledge of SOTA models in time series reasoning (based on cross-attention and multi-modal embedding),  timeseries agentic systems, time seriesmemoryand RAG. 

• Parameter-efficient fine-tuning,LoRA/Q-LoRA, and reward-based optimization methods (DPO, PPO, RLAIF). 

• Strong knowledge in statistical hypothesis testing, experimental design, causaldiscovery. 

• 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.