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PV/STA Engineer

2 months ago

Bengaluru, India Dew Software Full time

Dew Software is seeking a talented PV/STA Engineer to join our team. As a PV/STA (Post Verification/Silicon Test and Analysis) Engineer, you will be responsible for ensuring the quality and reliability of our semiconductor products. You will work closely with cross-functional teams, including design, test, and product engineering, to validate and analyze the performance and functionality of our products. Join our team at Dew Software and contribute to the development of cutting-edge semiconductor solutions that drive innovation and empower our clients.


Responsibilities
  • Perform post-verification testing to validate the functionality and performance of semiconductor products
  • Analyze test data and generate detailed reports outlining measurement results and findings
  • Collaborate with design and test teams to identify and troubleshoot issues
  • Develop methodologies and tools for data analysis and interpretation
  • Design and implement test programs and test flows
  • Conduct statistical analysis and provide insights for improvements
  • Stay up-to-date with industry trends and advancements in semiconductor testing and analysis

Requirements

  • Constraint development – work with RTL team on clock constraint and timing exception. Reading through 3 party IP datasheet and work on timing constraint.
  • Partition budgeting – push down from FC level STA and fine tune.
  • Timing Model build and checklist for all the mode/corner.
  • FC timing convergence include intra and inter partition. Work with PEO on physical optimization.
  • Timing ECO including automated flow (tweaker/prime closure) and manual eco tcl, timing eco for checklist

Benefits

  • Azure Infrastructure Experience: Proficiency in managing Azure infrastructure components, including virtual machines, storage, and networking, to support AI model development and deployment.
  • CI/CD Pipeline Experience: Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines, including the automation of model deployment processes.
  • Containerization in the Cloud: Strong knowledge of containerization technologies in the cloud, such as Docker and Kubernetes, for efficient deployment and scaling of machine learning models.
  • Machine Learning Expertise: Proficient in building and optimizing machine learning models, with a deep understanding of various ML algorithms and frameworks.
  • Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python and libraries like TensorFlow and PyTorch.
  • Data Management: Experience in data preprocessing, feature engineering, and data pipeline development for machine learning.
  • Collaborative Team Player: Excellent communication skills and the ability to work collaboratively with cross-functional teams, including AI engineers and SREs.
  • Documentation: Effective documentation skills to maintain clear and organized records of models, infrastructure configurations, and incident responses.

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