Staff AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs)
Core
Develop architectures for physically-intelligent reasoning models that unify multimodal sensor data (electrical, audio, motion, photonic, physiological) into coherent foundation models for context-aware time-series reasoning.
Role type
Staff AI/ML Engineer (Time-Series & Sensor Foundation Models)
Builds
Physically-intelligent reasoning models for the Faraday suite, deployed at the edge for automotive, health, industrial systems, and robotics.
Domain
Semiconductor, Edge Intelligence, Signal Processing, Time-Series AI
Deliverable
production ML models
Required skills
Time-series ML, signal processing, foundation models, representation learning, time series encoding/compression, cross-attention, multi-modal embedding, parameter-efficient fine-tuning (LoRA/Q-LoRA), reinforcement learning (DPO/RLAIF), statistical hypothesis testing, causal discovery, Python, PyTorch, large-scale distributed training
Preferred skills
Ph.D. in EE/CS/Applied Physics, patent/publication record, embedded systems leadership
Technologies
Chronos, TimesFM, TimeGPT, AWS, GCP
Responsibilities
Lead R&D on intelligent time-series agents for edge, advance sensor fusion research, create benchmarking pipelines, apply alignment/fine-tuning methods, co-design architectures with hardware teams, design statistical experiments, publish at major ML venues, mentor junior researchers
Seniority
Staff, hands-on IC with strategy & mentorship
