Job description

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today s innovators stay Ahead of What s Possible. Learn more at www.analog.com and on LinkedIn and X . 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 10 years of experience developing software products with an 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.) – 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. Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group. EEO is the Law: Notice of Applicant Rights Under the Law . 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 $230,000 to $316,250. 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.

Requirements

Must-haves

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
10 years of experience developing software products with an AI/ML focus
6 years of experience developing AI/ML products
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.
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.
EEO is the Law: Notice of Applicant Rights Under the Law .

Nice-to-haves

Preferred Education and Experience

Application prep

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Keywords to mirror

aimlpythonawsgcpgo

Platform: workday
Effort: long

Benefits

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Dental

Vision

401k