Principal AI/ML Engineer, Deep Agentic Reasoning Engineer (Lorenz Labs)
Company websiteanalog.com (Analog Devices, opens in new tab)
Base salary $230,000 to $316,250
Employer-published annual base salary, USD. Advertised base range in Wilmington, MA +1 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationWilmington, MA +1 more
- WorkplaceWorkplace not stated
- Status at last checkSource accepting applications when checked
- Last checked (25 min ago) · observed since
All places named
- Wilmington, MA
- US, CA, San Jose, Rio Robles
About this job
About 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, 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 www.analog.com and on LinkedIn and X.
About the Role
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.
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.
Prototype and Innovate: Prototype models in Python (using PyTorch/TensorFlow) 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.
What You Bring
Technical Mastery: 10+ 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).
Core Technical Expertise
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., LangGraph) and dynamic multi-agent harnesses for complex reasoning workflows
Growth Mindset
Appetite for continuous learning with demonstrated ability to quickly adapt to new research directions
Resourcefulness in exploring and implementing emerging paradigms such as JEPA training, world models, diffusion-based generation, and agentic harnesses
Interest in frontier applications including edge AI deployment, multi-agent orchestration, deep research automation
For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.
Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.
EEO is the Law: Notice of Applicant Rights Under the Law.
Job Req Type: ExperiencedRequired Travel: Yes, 10% of the timeShift Type: 1st Shift/DaysThe expected wage range for a new hire into this position is $230,000 to $316,250.Actual wage offered may vary depending on work location, experience, education, training, external market data, internal pay equity, or other bona fide factors.
This position qualifies for a discretionary performance-based bonus which is based on personal and company factors.
This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time, and other benefits.
Source: Analog Devices on Workday · View original posting (opens in new tab)
Pay in context
This role's base floor ($230,000) is above 76% of the 1,514 Data & ML roles in California listed on this site; 28 others list the same floor. Their median floor is $187,500; the middle half runs from $159,200 to $229,375.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.
This listingPrincipal AI/ML Engineer, Deep Agentic Reasoning Engineer (Lorenz Labs)$230,000 to $316,250
Principal Product Manager, Physical AI$230,000 to $316,250
Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs)$230,000 to $316,250
Forward Deployed Engineer$230,000 to $316,250
Senior Field Technical Leader, Consumer$227,700 to $313,088
Head of Application Security$219,200 to $301,400
Principal Engineer, Data Engineering$216,643 to $250,920
Chart as a table
| Role | Published base |
|---|---|
| Principal AI/ML Engineer, Deep Agentic Reasoning Engineer (Lorenz Labs) (this listing) | $230,000 - $316,250 |
| Principal Product Manager, Physical AI | $230,000 - $316,250 |
| Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs) | $230,000 - $316,250 |
| Forward Deployed Engineer | $230,000 - $316,250 |
| Senior Field Technical Leader, Consumer | $227,700 - $313,088 |
| Head of Application Security | $219,200 - $301,400 |
| Principal Engineer, Data Engineering | $216,643 - $250,920 |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-10-29
Where this pay comes from
Location as the employer wrote it: US, CA, San Jose, Rio Robles • US, MA, Wilmington
The expected wage range for a new hire into this position is $230,000 to $316,250.
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
