Sr Principal Software Engineer, Innovus — Physical Synthesis & Optimization

Cadence Design Systems•San Jose, CA

1d•$161,000 - $299,000•Remote

About The Position

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Cadence sits at the intersection of semiconductor innovation and high-performance computing. Through Design for AI, our EDA technologies enable the advanced silicon platforms powering modern AI infrastructure. Through AI for Design, we integrate machine learning, data-driven optimization, and intelligent automation into our industry-leading implementation flows to transform how chips are designed. As part of the Innovus R&D team, you will build high-performance software engines and algorithms behind next-generation physical synthesis, helping push the boundaries of performance, power, area (PPA), timing closure, and design productivity at scale. We are looking for a Senior / Staff Software Engineer to contribute to the design and development of next-generation physical synthesis and optimization engines within Innovus. In this hands-on engineering role, you will solve challenging computational problems at the intersection of advanced graph/spatial algorithms, high-performance C++, parallel and distributed computing, and large-scale numerical optimization. Your work will operate at semiconductor scale—where algorithmic efficiency, memory footprint, and multi-core scalability directly impact the ability to design billions of transistors on advanced process nodes.

Requirements

Master’s or Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, or a related quantitative field (or equivalent hands-on industry experience).
Strong hands-on experience developing high-performance, production-quality C++ (C++17/20) in Linux environments.
Solid foundation in data structures, graph algorithms, computational geometry, memory management, and software architecture.
Practical experience with multithreaded or concurrent programming and low-level performance profiling.
Proven ability to analyze, debug, and optimize complex algorithmic bottlenecks in large-scale software systems.

Nice To Haves

Prior experience developing EDA tools, with specific knowledge in physical synthesis, global/detail placement, static timing analysis (STA), routing, or timing closure.
Exposure to applying machine learning, deep learning, reinforcement learning, or data-driven heuristics to software optimization problems.
Proficiency with Python, Tcl, or shell scripting for benchmarking and workflow automation.
Experience with SIMD vectorization, GPU/CUDA acceleration, or large-scale distributed compute frameworks.

Responsibilities

Design, implement, unit-test, and optimize high-performance C++ engines for physical synthesis, placement, and timing-driven optimization.
Develop advanced graph algorithms, spatial data structures, and continuous/combinatorial optimization techniques for massive-scale physical design workloads.
Identify and eliminate CPU bottlenecks, memory footprint overhead, and algorithmic scaling limits in production C++ codebases.
Translate research concepts and mathematical formulations into robust, maintainable, production-quality software.
Implement multi-threaded and distributed algorithms for computationally intensive implementation engines.
Scale physical design workloads across multi-core systems and compute clusters while minimizing synchronization overhead and memory bandwidth constraints.
Apply modern profiling and benchmarking tools to maximize CPU utilization, cache efficiency, and execution throughput.
Develop physical design optimization modules addressing timing, power, performance, area (PPA), and congestion.
Work directly with large-scale layout and netlist data to build practical, highly scalable optimization strategies.
Collaborate with R&D colleagues to integrate data-driven and AI-assisted optimization heuristics into core synthesis flows.