Senior Principal AI​/ML Engineer, Deep Agentic Reasoning Engineer; Lorenz Labs

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.

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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  and on Linked In and X.

We’re looking for Deep Agentic Reasoning Engineer (open rank) to design and build multimodal reasoning models (e.g., time-series, audio, video, sensor data) for Edge AI applications. You’ll collaborate with experts in AI, NLP, audio, time series, RL and other domains to advance reasoning capabilities in alignment with Analog Devices business needs. This role offers the opportunity to work on the forefront of AI research, contributing to the development of agentic systems and multimodal reasoning frameworks.

• Develop and Evaluate Models: build and evaluate deep learning models for multimodal classification and reasoning tasks.
• Full Research Stack:
  Engage in the full research lifecycle, including stakeholder discussions, data collection, annotation, preprocessing, model training, and rigorous evaluation.
• Prototype and Innovate:
  Prototype models in Python (using PyTorch/Tensor Flow) and implement core reasoning improvements, including mid-training and post-training techniques for multimodal models.
• Knowledge Distillation:
  Apply distillation techniques to transfer reasoning capabilities from larger models to smaller, efficient models.
• Dissemination:
  Document experiments and results through internal reports, blog posts, and publications in top-tier conferences and workshops.

• Technical Mastery: 15+ years of experience releasing software products with a strong track record of AI/ML success.
• AI/ML Expertise: 6+ years of experience releasing AI/ML products.
• Educational Background:
  PhD in AI-related fields such as NLP, Computer Vision, Speech Processing, or related discipline.
• Research Excellence:
  Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, CVPR).

• Proficiency in training, post-training, and evaluating transformer-based models with deep understanding of training dynamics, architecture modifications, and optimization techniques
• Strong expertise in supervised fine-tuning, RL (e.g., GRPO), and advanced post-training techniques for reasoning models, including hands-on experience with RL implementation and policy optimization
• Experience in implementing and modifying transformer architectures (encoder/decoder variants, attention mechanisms, positional encodings)
• Advanced knowledge of techniques including Chain-of-Thought (CoT), agentic tree search methods, continual learning, etc.
• Multi-agent orchestration, task decomposition, and experience with both static frameworks (e.g., Lang Graph) and dynamic multi-agent harnesses for complex reasoning workflows

• Appetite…