About the Role 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.
Key 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.
- Besides the time series modality these models will be able to use other modalities such as text and image, which will serve as additional context.
- 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.
Must Have Skills- 14+ 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.
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.
For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position - except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) - may have to go through an export licensing review process.
Job Req Type: Experienced
Required Travel: Yes, 10% of the time
Shift Type: 1st Shift/Days
The expected wage range for a new hire into this position is $260,360 to $357,995.
- Actual wage offered may vary depending on work location, experience, education, training, external market data, internal pay equity, or other bona fide factors.
- This position qualifies for a discretionary performance-based bonus which is based on personal and company factors.
- This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time, and other benefits.
