R&D GenAI/ML Engineer

at  Synopsys

Seeking a highly motivated and innovative R&D GenAI/ML Engineer. The candidate would be working as part of a highly experienced ASIC mixed-signal design and verification team, targeting the current and next generation USB/PCIe/DPHY/CDPHY/MPHY/DP/HDMI products, applying AI and machine learning models and algorithms to address complex business needs.
The position offers an excellent opportunity to work with a skilled team of digital and mixed signal engineers delivering high-end mixed-signal designs from specification development to performing functional and performance tests on the test-chips.

KEY QUALIFICATIONS:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related field.
• Strong understanding and expertise in ML/AI.
• 3+ years of hands-on experience in AI and ML solutions
• Knowledge of semiconductor manufacturing process is a plus.
• Scripting and programming skills: Unix Shell, Python, C++, Perl, TCL is a plus
• Strong understanding of statistical analysis, data mining, and data visualization techniques.
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Kubernetes).
• Familiarity with version control systems (e.g., Git) and software development methodologies (e.g., Agile, Scrum)
• Strong desire to learn and explore new technologies.
• English language working proficiency and communication skills allowing teamwork in an international environment.
• Willingness to work in a distributed international team.

• AI/ML Solution Development: Design, develop, and deploy AI and machine learning models and algorithms to solve complex business problems.
• Technical Leadership: Provide technical leadership and mentorship to junior engineers and data scientists, guiding them in best practices and advanced techniques.
• Research & Innovation: Stay up to date with the latest advancements in AI and machine learning technologies and apply them to improve existing systems or develop new solutions.
• Collaboration: Work closely with R&D engineers, and other stakeholders to define project requirements, create technical specifications, and ensure successful implementation of AI solutions.
• Data Analysis & Preprocessing: Perform data analysis, data preprocessing, and feature engineering to prepare datasets for machine learning models.
• Model Training & Evaluation: Train, validate, and fine-tune machine learning models, ensuring they meet performance and accuracy requirements.
• Deployment & Monitoring: Deploy AI models into production environments, and monitor their performance, making adjustments as necessary to maintain optimal operation.
• Documentation: Document AI models, algorithms, and methodologies to ensure reproducibility and knowledge sharing within the team.
• Compliance & Ethics: Ensure AI solutions adhere to ethical guidelines, data privacy regulations, and industry standard