Deep Agentic Reasoning Engineer (Lorenz Labs)

About the role

Analog Devices (NASDAQ: ADI) is a global semiconductor leader bridging the physical and digital worlds. Lorenz Labs, ADI’s advanced AI engineering group within Edge AI, is developing foundation models and agentic systems for Physical Intelligence, aiming to realize an Artificial Engineer capable of understanding, simulating, and designing electro‑physical systems. The Deep Agentic Reasoning Engineer will design and build multimodal reasoning models (time‑series, audio, video) for Edge AI applications, collaborating with experts in AI, NLP, audio, time series and other domains to advance reasoning capabilities aligned with business needs.

Responsibilities

• Develop and evaluate deep learning models for multimodal classification and reasoning tasks.
• Engage in the full research lifecycle: stakeholder discussions, data collection, annotation, preprocessing, model training, and rigorous evaluation.
• Prototype models in Python (PyTorch/TensorFlow) and implement core reasoning improvements, including mid‑training and post‑training techniques for multimodal models.
• Apply knowledge distillation 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.

Requirements

• PhD in an AI‑related field such as NLP, Computer Vision, Speech Processing, or a related discipline.
• Strong publication record in top‑tier venues (e.g., NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, CVPR, AAAI).

Skills

• 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 (text, images, audio, video).
• Familiarity with agentic reasoning systems, including task orchestration and tools such as Langchain/LangGraph.
• Know‑how of reinforcement learning approaches for reasoning models (e.g., GRPO).
• Strong understanding of LLM evaluation techniques.

Benefits

• Medical, vision and dental coverage.
• 401(k) plan.
• Paid vacation, holidays, and sick time.
• Other benefits.
• Discretionary performance‑based bonus based on personal and company factors.

Pay

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.

Schedule

• Shift type: 1st Shift/Days.
• Required travel: Yes, approximately 10% of the time.