Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models

Analog Devices

. Lead R&D on intelligent time-series agents for edge by combining anomaly detection, reasoning, and forecasting foundation models .

Posted 10/14/2026full-timeUnited StatesLead💰 $230,000 - $316,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates deep expertise in time-series machine learning, signal processing, and foundation models, with a strong focus on developing and fine-tuning multimodal models for edge applications. Proven ability to lead R&D efforts, mentor researchers, and translate complex research into practical solutions.

Highest-signal resume keywords

Time-Series Machine LearningSignal ProcessingFoundation ModelsPythonPyTorch

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Anomaly DetectionReasoningForecastingRepresentation LearningTime-Series EncodingTime-Series CompressionMotif DiscoveryStatistical Hypothesis TestingExperimental DesignCausal Discovery

Soft Skills

LeadershipCollaborationMentoringAgility

Tools & Technologies

AWSGCPLoRAQ-LoRADPOPPORLAIF

Certifications & Qualifications

Ph.D. in Electrical EngineeringPh.D. in Computer SciencePh.D. in Applied Physics

Industry Keywords

Multimodal ModelsSensor FusionCross-Modal AlignmentTime-Series Agentic SystemsReal-Time Sensing ArchitecturesBenchmarking PipelinesPhysical Intelligence SystemsNeurIPSICLRICML

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Lead R&D on intelligent time-series agents for edge by combining anomaly detection, reasoning, and forecasting foundation models
Incorporate electrical, audio, motion, physiological, text, and image modalities into multimodal models
Advance sensor fusion and cross-modal alignment across electrical, acoustic, inertial, and photonic domains
Create benchmarking pipelines for cross-domain time-series foundation models, including representation robustness, interpretability, and hardware performance metrics
Apply LoRA, Q-LoRA, adapter-tuning, and contrastive alignment to multimodal sensor datasets
Leverage time-series embedding and compression research for edge reasoning models
Investigate DPO and RLAIF alignment techniques for physical and sensory reasoning tasks
Partner with hardware, signal processing, and systems teams to co-design real-time, energy-efficient sensing architectures
Design statistical experiments for subject-matter experts to collect sensor data for model development
Publish and represent Analog Devices at ML and signal-processing venues including NeurIPS, ICLR, ICML, ICASSP, and KDD
Mentor junior researchers and help shape Lorenz Labs’ foundation-model strategy

Requirements

What you’ll need

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, including Chronos, TimesFM, and TimeGPT
Hands-on experience training or fine-tuning one or more time-series foundation models
Experience evaluating different time-series foundation models
Proficiency in representation learning, time-series encoding, time-series compression, and motif discovery in high-dimensional temporal data
Knowledge of state-of-the-art models in time-series reasoning, time-series agentic systems, time-series memory, and RAG
Experience with parameter-efficient fine-tuning, LoRA/Q-LoRA, DPO, PPO, and RLAIF
Strong knowledge of statistical hypothesis testing, experimental design, and causal discovery
Fluency in Python and PyTorch
Experience with large-scale training pipelines using cloud or distributed systems such as AWS and GCP
Ability to collaborate across ML, hardware, and embedded systems disciplines and translate research into deployable physical intelligence systems
Ph.D. in Electrical Engineering, Computer Science, or Applied Physics is preferred
Demonstrated leadership and agility in combining technical solutions with business problems, preferably for embedded systems
Record of innovation through patents, publications, or open-source contributions
Applicants other than US Citizens, US Permanent Residents, and protected individuals may have to undergo an export licensing review process
Required travel: 10% of the time

Benefits

Comp & perks

Discretionary performance-based bonus based on personal and company factors
Medical coverage
Vision coverage
Dental coverage
401k
Paid vacation
Paid holidays
Sick time
Other benefits