Verified Job
Hardware / Network Engineer
Distributed Systems Engineer- job post Cadence Design Systems
Hardware / Network Engineer
#780160
Remote / WFH
Cadence Design Systems
Job Description
Full job description
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
About the Role
We're building a next-generation distributed transistor-level electromigration and IR drop analysis tool. Our team has strong expertise in numerical solvers and circuit simulation algorithms. We're looking for a motivated distributed systems engineer to help build the scalable data processing infrastructure for handling massive circuit designs across distributed computing resources.
What You'll Build
You'll contribute to the core distributed infrastructure for a Python-based platform orchestrating high-performance C++ solvers, working on:
Data Pipeline & I/O Management
Build ingestion pipelines for large-scale netlists and simulation data
Implement high-performance I/O for multi-TB circuit databases
Develop serialization/deserialization layers bridging Python and C++ components
Design streaming interfaces for distributed solver results
Job Orchestration & Workflow
Implement task distribution with fault-tolerant scheduling for long-running simulations
Develop resource management and load balancing across compute clusters
Build monitoring and observability for distributed workflows
Optimize task granularity and dependency management
Visualization & Analytics
Develop scalable visualization for multi-dimensional TB-scale simulation results
Implement interactive data exploration with optimization techniques (downsampling, LOD, progressive rendering)
Required Expertise
Distributed Systems
3+ years building distributed systems with Python
Experience with distributed computing frameworks (Dask, Spark, Ray, or Celery)
Understanding of distributed computing patterns, data locality, and fault tolerance
Data Engineering
Experience with high-performance data formats (HDF5, Parquet, Arrow, or similar columnar formats)
Familiarity with data partitioning strategies and streaming patterns
Some exposure to Python/C++ interop (pybind11, nanobind)
Software Engineering
Strong Python, C++ programming skills with production code experience
Comfortable working in large codebases and col
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
About the Role
We're building a next-generation distributed transistor-level electromigration and IR drop analysis tool. Our team has strong expertise in numerical solvers and circuit simulation algorithms. We're looking for a motivated distributed systems engineer to help build the scalable data processing infrastructure for handling massive circuit designs across distributed computing resources.
What You'll Build
You'll contribute to the core distributed infrastructure for a Python-based platform orchestrating high-performance C++ solvers, working on:
Data Pipeline & I/O Management
Build ingestion pipelines for large-scale netlists and simulation data
Implement high-performance I/O for multi-TB circuit databases
Develop serialization/deserialization layers bridging Python and C++ components
Design streaming interfaces for distributed solver results
Job Orchestration & Workflow
Implement task distribution with fault-tolerant scheduling for long-running simulations
Develop resource management and load balancing across compute clusters
Build monitoring and observability for distributed workflows
Optimize task granularity and dependency management
Visualization & Analytics
Develop scalable visualization for multi-dimensional TB-scale simulation results
Implement interactive data exploration with optimization techniques (downsampling, LOD, progressive rendering)
Required Expertise
Distributed Systems
3+ years building distributed systems with Python
Experience with distributed computing frameworks (Dask, Spark, Ray, or Celery)
Understanding of distributed computing patterns, data locality, and fault tolerance
Data Engineering
Experience with high-performance data formats (HDF5, Parquet, Arrow, or similar columnar formats)
Familiarity with data partitioning strategies and streaming patterns
Some exposure to Python/C++ interop (pybind11, nanobind)
Software Engineering
Strong Python, C++ programming skills with production code experience
Comfortable working in large codebases and col
