Deep Agentic Reasoning Engineer (Lorenz Labs)
About The Position
Lorenz Labs, ADI’s advanced AI engineering group within Edge AI, is pioneering the frontier of Physical Intelligence—developing foundation models and agentic systems that can reason about the physical world. We are building the next generation of models that go beyond language and vision, into time, signals, and embodied experience. Our long-term ambition is the realization of an Artificial Engineer: an AI capable of understanding, simulating, and designing electro-physical systems with human-like intuition—complemented by the development of highly optimized embedded models for Edge AI. We’re looking for a Deep Agentic Reasoning Engineer (open rank) to design and build multimodal reasoning models (e.g., time-series, audio, video) for Edge AI applications. You’ll collaborate with experts in AI, NLP, audio, time series 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.
Requirements
- PhD in AI-related fields such as NLP, Computer Vision, Speech Processing, or related discipline.
- Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, CVPR, AAAI).
- Proficiency in building and evaluating transformer-based models.
- Expertise in supervised fine-tuning and post-training techniques for reasoning models.
- Experience with multimodal reasoning models (e.g., combining text, audio, and video data).
- Familiarity with Chain-of-Thought (CoT) reasoning and its applications in LLMs.
- Experience with multimodal reasoning frameworks and integrating diverse data types (e.g., text, images, audio, video).
- Familiarity with agentic reasoning systems, including task orchestration and popular tools such as Langchain/langgraph.
- Know-how of reinforcement learning approaches for reasoning models (e.g. GRPO).
- Strong understanding of LLM evaluation techniques.
Responsibilities
- Develop and evaluate deep learning models for multimodal classification and reasoning tasks.
- Engage in the full research lifecycle, including stakeholder discussions, data collection, annotation, preprocessing, model training, and rigorous evaluation.
- Prototype models in Python (using PyTorch/TensorFlow) and implement core reasoning improvements, including mid-training and post-training techniques for multimodal models.
- Apply distillation techniques to transfer reasoning capabilities from larger models to smaller, efficient models.
- Document experiments and results through internal reports, blog posts, and publications in top-tier conferences and workshops.
Benefits
- medical, vision and dental coverage
- 401k
- paid vacation, holidays, and sick time
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What This Job Offers
Job Type
Full-time
Career Level
Executive
Education Level
Ph.D. or professional degree
