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

Analog Devices

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

Posted 10/14/2026full-timeUnited StatesSenior💰 $260,360 - $357,995 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in time-series machine learning and signal processing, with proficiency in developing and fine-tuning foundation models. Capable of leading R&D efforts to create intelligent edge applications through multimodal integration and advanced statistical methodologies.

Highest-signal resume keywords

Time-Series Machine LearningSignal ProcessingFoundation ModelsPython and PyTorchStatistical Hypothesis Testing

ATS Keywords

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Hard Skills

Time-Series EncodingTime-Series CompressionMotif DiscoveryLoRA/Q-LoRADPO and RLAIFCross-Attention ModelsMultimodal EmbeddingParameter-Efficient Fine-TuningExperimental DesignCausal Discovery

Soft Skills

LeadershipCollaborationMentoring

Tools & Technologies

AWSGCPLarge-Scale Training Pipelines

Certifications & Qualifications

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

Industry Keywords

Anomaly DetectionSensor FusionMultimodal ModelsEdge ApplicationsPhysical Intelligence Systems

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Lead R&D creating intelligent time-series agents for edge applications 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
Use state-of-the-art 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 research and represent Analog Devices at major machine learning and signal-processing venues, including NeurIPS, ICLR, ICML, ICASSP, and KDD
Mentor junior researchers and help shape Lorenz Labs’ foundation-model strategy for understanding and reasoning about physical systems

Requirements

What you’ll need

15+ years of experience developing software products with a strong AI/ML focus
6+ years of experience developing AI/ML products
Deep expertise in time-series machine learning, 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 time-series reasoning models based on cross-attention and multimodal embedding
Knowledge of time-series agentic systems, time-series memory, and RAG
Experience with parameter-efficient fine-tuning, LoRA/Q-LoRA, and reward-based optimization methods including 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 or 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 be subject to export licensing review for access to technical data
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