Staff Machine Learning Engineer Full-time

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 $12 billion in FY23 and approximately 26,000 people globally working alongside 125,000 global customers, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn.

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 $12 billion in FY23 and approximately 25,000 people globally working alongside 125,000 global customers, ADI ensures today’s innovators stay Ahead of What’s Possible.

The AI Incubation team is looking for an experienced Staff Engineer to develop core AI technologies for Analog Devices’ future Signal Chain AI products.

Responsibilities include:

• Participate in all phases of our applied research projects in ML enabled DSP: ideation through to production.
• Create sandbox simulations, deployable implementations, and evaluation frameworks for algorithm performance and robustness.
• Work with production coding counterparts to develop process to transition successful PoC code.
• Stay abreast of the latest developments in machine learning and robotics from reputable groups.

Qualifications:

• Master’s degree in electrical engineering, mechanical engineering, physics, computer science, robotics, or a related field; Ph.D. preferred.
• 5 years of full-time industry experience.
• Knowledge of digital signal processing, control theory and its application with focus on AI enabled DSP.
• Strong proficiency in programming languages such as Python, Rust, C++.
• Strong background in machine learning, deep neural networks, reinforcement learning, and other AI technologies
• Hands-on experience designing, training, fine-tuning, and deploying embedded deep learning networks.
• Experience with software development best practices, including version control systems like Git, testing, and continuous integration.
• Excellent problem-solving skills and ability to work collaboratively in an interdisciplinary team environment.
• Strong communication skills, both written and verbal.

Preferred Qualifications:

• Well versed in concepts of audio signal processing (time & frequency domain processing, filtering, Fourier transform etc.)
• Knowledge of deep learning architectures for audio applications like audio enhancement, noise reduction, speech recognition, event detection/classification.

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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.

Job Req Type: Experienced

Required Travel: Yes, 10% of the time

Shift Type: 1st Shift/Days