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Cadence

Do you want to help invent the future of computer-aided design automation using machine learning techniques?  Cadence is seeking highly motivated software architects, experienced with machine learning, who can think outside the box and develop new and unique solutions. This is an opportunity for you, as part of the original team, to innovate in a new market for machine learning and trail blaze new solutions, especially in the custom semiconductor design market segment.

 

The ideal candidate will have experience evaluating multiple complex technical solutions in the machine learning area and clearly communicating to a distributed, cross-functional team the relative merits and concepts. You have a pragmatic, customer-focused approach to architecture, design, and development, and will set technical direction for a new platform of solutions. You have significant experience in applying machine learning to a variety of technical areas, including areas without ‘big data’ in the traditional sense. You have experience with supervised, unsupervised, and reinforcement learning, and have developed, deployed, and supported applications utilizing these capabilities in Python and C/C++.

 

Responsibilities:

Drive architecture and design decisions for the product and platform focusing on the machine learning flow
Provide technical leadership on projects, including software engineering practices
Lead key architecture and design decisions, including gathering requirements and input from a cross-functional team and considering long-term effectiveness and interoperability
Analyze data sets for utility in unsupervised and supervised learning activities, perform failure analysis and feature engineering to improve performance, communicates these findings in a clear and thoughtful manner
Leads team and influences other teams in understanding and appropriately utilizing machine learning techniques into traditional software functionality
Lead software system architecture and design to appropriately incorporate machine learning elements into product features and can effectively communicate this solution to software engineers and business leaders
Plan, architect, design, develop, test and maintain key software enhancements, especially related to machine learning capabilities
Drive design decisions for the product and platform focused on machine learning flow and be responsible for seeing those decisions implemented successfully
Troubleshoot and resolve system problems and customer issues, including complex systemic interactions and performance problems

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