Job Details:

Job Description:

Become a member of our world-class software research and development team! Altera® develops programmable logic technologies to accelerate innovation for many customers worldwide.

You will be architecting and developing leading-edge software innovations for Quartus, the tool that optimizes our FPGA devices, within a research-oriented team. The Quartus Placement optimization engines are key to unlocking high performance, area and power efficiency for our customer's design applications.

As part of the Quartus Placement team, your role will include:

• Leading research & development efforts to explore novel optimization algorithms for our FPGA CAD software tools, including timing-driven analytic placement, detailed placement, partitioning and floorplanning

• Developing and optimizing the software to drive performance improvements by leveraging innovative FPGA hardware features

Ideal candidates exhibit the following behavioral traits:

• Intellectual curiosity and a passion for exploring new technology

• Excellent problem-solving, debugging, and attention to detail

• Great communication, teamwork, and interpersonal skills

Qualifications:

Minimum Requirements:

• Degree in Electrical Engineering, Computer Engineering, Computer Science or related field.

• MS + 15 years of industry software experience, or PhD + 10 years of industry software experience

Desired/Preferred Skills:

• Significant experience coding & hands-on development of high performance multi-core software systems

• Extensive experience as an architect/technical lead for developing EDA placement optimization algorithms

• Proven leadership skills for collaborative cross functional projects

• Experience with Altera® Quartus or AMD Vivado software

• Experience with combinatorial/continuous optimization, including but not limited to Boolean SAT, stochastic search-based methods, numerical methods for continuous optimization, dynamic programming, and applications to FPGA placement

• Experience with NOC optimization for FPGA placement

• Experience with applying machine learning techniques to EDA software

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