Develop AI and Data Science based solutions to build state-of-the-art solutions for silicon design verification and firmware validation. Automate data pipelines and develop tools to support regression analysis, bug triaging, and root cause analysis. Participate in end-to-end project scoping and stakeholder discussions to determine technical merit of the idea, vale proposition and resource requirements. Education: Master's or PhD in Computer Science, Electrical Engineering, or a related field. Experience: 8+ years in data science and machine learning with at least 2 years in semiconductor verification environment In-depth understanding of Statistics, classical ML and deep learning, and the mathematics and formulation behind these algorithms. Well versed with text processing, various methodologies in data embedding, NLP techniques and recent advancements in GenAI and LLMs. Hands-on experience with optimization and reinforcement learning based algorithms. Solid understanding of data engineering pipeline for deployment and MLOps. Proficiency in programming languages such as Python, R, and SQL. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data visualization tools (e.g., Tableau, Power BI). Strong understanding of digital design and verification concepts (e.g., RTL, UVM, coverage metrics, simulation). Experience with EDA tools (e.g., Synopsys VCS, Cadence Xcelium, Mentor Questa) and verification flows is a great plus. Knowledge of hardware description languages (Verilog/SystemVerilog). Experience with CI/CD pipelines and MLOps practices. Patents or publications in relevant fields.
