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

Originally posted 11 September 2026 by the employer.

This role advances AI engineering at the intersection of sensing, signal intelligence, and large-scale temporal modeling.

About the role

This role is for a Principal AI Engineer in Time-Series & Sensor Foundation Models, advancing AI engineering at the intersection of sensing, signal intelligence, and large-scale temporal modeling. Your work will contribute directly to ADI’s Faraday suite of physically-intelligent reasoning models.

What you'll do

• Lead R&D on creation of intelligent time-series agents for edge by combining time series anomaly detection, reasoning, forecasting foundation models.
• 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.
• 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.

What you'll need

• 15+ years of experience developing software products and a strong AI/ML focus.
• 6+ years of experience developing AI/ML products.
• Deep expertise in time-series ML, signal processing, and foundation models (Chronos, TimesFM, TimeGPT, etc.).
• 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.

Nice to have

• 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.

Skills: time-series ML, sensor foundation models, multimodal sensor data, edge intelligence, physically-intelligent reasoning models