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

Key Responsibilities

Lead R&D on creation of intelligent time-series agents for edge by combining time series anomaly detection, reasoning, forecasting foundationmodels; thesemodels will be able to incorporate multiple data modalities such as electrical, audio, motion, physiological as well as text.

Besides the time seriesmodalitythese models will be able to use other modalities such as text and image, which will serve asadditionalcontext.

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, coveringrepresentationrobustness, interpretability, and hardware performance metrics.

Apply alignment and fine-tuning methods such asLoRA, Q-LoRA, adapter-tuning, and contrastive alignment for multimodal sensor datasets.

LeveareSOTA 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 ondesignof 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 reasonaboutphysical systems.

Must Have Skills

6+ years of experience developing software products with an AI/ML focus

• 4+ years of experience developing AI/ML products

Deepexpertisein 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.

Proficiencyin representation learning, time series encoding, time seriescompressionand motif discovery in high dimensional temporal data.

Knowledge of SOTA models in time series reasoning (based on cross-attention and multi-modal embedding), timeseries agentic systems, time seriesmemoryand RAG.

Parameter-efficient fine-tuning,LoRA/Q-LoRA, and reward-based optimization methods (DPO, PPO, RLAIF).

Strong knowledge in statistical hypothesis testing, experimental design, causaldiscovery.

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 $197,800 to $271,975.

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.