Principal ML Engineer (Robotics)

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, 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).

Principal Machine Learning Engineer

Target hire: Feb 2026

The Dexterous AI Group (DAG) is seeking an experienced and innovative Machine Learning Engineer to pioneer the next generation dexterous robots. We are a team dedicated to achieving human-level dexterity: creating systems with rich sensing modalities, including vision, audio, tactile, spatial and temporal understanding powered by physical AI. You will develop groundbreaking multimodal ML models that leverage ADI’s cutting-edge sensor and robotic innovations to solve high-value industrial challenges and redefine what's possible.

Responsibilities:

• Lead the development of robot learning models (RL, IL) for dexterous manipulation.
• Design and implement simulation environments and evaluation frameworks for algorithm validation.
• Optimize and deploy trained policies onto real-world robots for autonomous tasks.
• Identify and solve system-level performance bottlenecks on task completion.
• Contribute to the full data lifecycle, from collection and curation to pipeline optimization, across proprioceptive, visual, 3D, force, and tactile data streams.
• Collaborate on building reusable software frameworks for the team.
• Stay abreast of the latest developments in machine learning and robotics from reputable groups.

Required Qualifications:

• Master’s degree or equivalent experience in a relevant field.
• 8+ years of experience in robot learning research or engineering.
• Strong background in ML/AI, including Reinforcement Learning and Imitation Learning.
• Expertise in training large-scale models with PyTorch on multi-GPU clusters (e.g., DeepSpeed).
• Proficiency in software development for deployable ML systems.
• A track record of relevant publications in top international conferences (RSS, NeuRIPS, ICML, ICLR, CoRL, ICRA, IROS, …)

Preferred Qualifications:

• Ph.D. in a relevant field.
• Familiarity with foundation models, world models, or VLAs.
• Experience with robotics toolkits like ROS/ROS2, Isaac Sim, etc.

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.

• 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.