Deep Agentic Reasoning Engineer (Lorenz Labs)
Analog Devices · San Jose, Rio Robles, CA
About this role
Analog Devices is hiring a mid-level Machine Learning Engineer based in San Jose, Rio Robles, CA. The posting calls out experience with Python, TensorFlow, PyTorch, LLMs. Listed education preference: a Ph.D. or equivalent. Compensation is listed at $170,775–$256,163 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Jose, Rio Robles, CA
- Education
- Ph.D. preferred
- Posted
- Apr 20, 2026
More roles at Analog Devices
Job description
from Analog Devices careersAbout Analog Devices
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, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at www.analog.com and on LinkedIn and Twitter (X).
Deep Agentic Reasoning Engineer
Location: Boston, MA or San Jose, CA
Summary
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.
What you’ll do:
- 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.