At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
At Cadence, we hire and develop innovators who want to make an impact on the world of technology.
The Cadence Virtuoso platform powers all of the latest analog and mixed-signal design innovations in consumer, mobile and enterprise electronics worldwide. We are looking for talented software engineers to join our team and contribute to creating the industry’s best analog and mixed-signal placement flow. Placers automatically arrange electrical components on a silicon wafer to minimize area, routing length or improve other performance metrics. Now is the time for innovation and you’ll have a great opportunity to make a difference by applying your creativity and problem-solving skills.
The ideal candidate has several years of work experience, but the position is open to candidates at all levels, including new and recent grads. Strong understanding of software data structure and algorithms and proficiency in C/C++ are basic job requirements.
The job is located in the thriving city of Pittsburgh, PA. Relocation assistance is available. B.S. or higher in computer science, engineering, physics, math or a related area is required.
Bachelors in Computer Science or Electrical Engineering + 4 years of related experience, or Masters + 2 years of related experience, or PhD + 0 years of related experience
Strong understanding of data structures, algorithms and databases.
Experience developing software on Linux platforms
Not Required but nice to have
Familiarity with or interest in analog or digital circuit design
Demonstrated proficiency in C++, gdb debugging, and general software development skills
Scripting Language experience (e.g. Python, SKILL, TCL, perl)
Exposure to the Cadence Virtuoso environment or other electronic design platforms.
Experience with GUI Frameworks such as QT
Exposure to SQL databases.
Exposure to build and version-control systems.
Coursework in circuit analysis, signal processing, statistics
Familiarity with Machine Learning Algorithms
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