Staff AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs) - CA, San Jose, Rio Robles, United States

Originally posted 11 September 2026 by the employer — open 8 days.

About the role

This Staff AI/ML Engineer will 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 into a coherent foundation for context-aware reasoning across time, contributing to ADI’s Faraday suite of physically-intelligent reasoning models.

What you'll do

• Lead R&D on intelligent time-series agents for edge by combining time series anomaly detection, reasoning, and 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 hardware, signal processing, and systems teams to co-design architectures for real-time, energy-efficient sensing applications.

What you'll need

• 6+ years of experience developing software products with an AI/ML focus.
• 4+ years of experience developing AI/ML products.
• Deep expertise in time-series ML, signal processing, and foundation models (Chronos, TimesFM, TimeGPT).
• 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.
• Experience with parameter-efficient fine-tuning, LoRA/Q-LoRA, and reward-based optimization methods (DPO, PPO, RLAIF).
• Strong knowledge in statistical hypothesis testing, experimental design, and causal discovery.
• Fluency in Python, PyTorch, and large-scale training pipelines using cloud or distributed systems (AWS, GCP).
• Ability to collaborate across disciplines—ML, hardware, and embedded systems—and translate research into deployable physical intelligence 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, Edge AI, multimodal sensor data, PyTorch