Distributed Systems Engineer

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.