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
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 programming skills with production code experience
Comfortable working in large codebases and collaborative development environments
Understanding of software engineering best practices (testing, code review)
Nice to Have
Background in EDA, VLSI, semiconductor design, or computational engineering
Experience with scientific/engineering data visualization
HPC experience with job schedulers (Slurm, PBS, LSF)
GPU acceleration knowledge
Familiarity with modern tools (Go, Plotly, Bokeh, Holoviews, Datashader)
Open-source distributed computing contributions
Experience with cloud platforms (AWS, GCP, Azure)
Why Join Us
We bring strong expertise in numerical methods and circuit analysis algorithms, well-defined solver interfaces, and a clear technical vision. You'll work alongside experienced engineers building greenfield distributed infrastructure with modern tools. This is an opportunity to grow your expertise in production-scale distributed systems while solving challenging problems in chip design.
What You'll Learn
Production distributed systems architecture and patterns
Large-scale data pipeline design and optimization
Performance engineering for multi-TB datasets
Building reliable, observable infrastructure
Working with domain experts in circuit simulation and numerical methods
Ideal Candidate
You're eager to deepen your distributed systems expertise and excited about data pipeline architecture. You have foundational experience with Python distributed computing and want to tackle production-scale challenges. You're comfortable learning new technologies, asking questions, and collaborating with both systems and domain experts. You value clean code, observability, and user experience alongside performance.
No circuit simulation expertise needed. We need your enthusiasm for building scalable, reliable infrastructure and your willingness to grow into a distributed systems expert.
Position Location
This is a full-time, on-site position based in Port Moody (Greater Vancouver Area) Canada. In-office attendance is required.
We’re doing work that matters. Help us solve what others can’t.
Distributed Systems Engineer • PORT MOODY 01
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