We Are
Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D; capability and productivity, powering innovation today that ignites the ingenuity of tomorrow.
You Are
You have built and scaled platforms that actually run in production, not proof-of-concepts that look good in slides. You know that Kubernetes is powerful, but also that it can become a mess fast if the design decisions are not made with scale and maintainability in mind.
When someone says "we need to deploy this new AI service," you do not just spin up a pod and call it done. You ask about traffic patterns, failure modes, and what happens when this thing needs to scale 10x in six months.
What You'll Be Doing
Perform complex development activities that may require extensive analysis in areas including cloud deployment and maintenance,
as well as distributed system maintenance and scaling
Use best practices and evangelize through RFCs and mentoring
Help scale our processes (release, development environments, CI/CD pipelines)
Root cause investigation, automated release testing, production incident solving
Design, deploy, and maintain the platform infrastructure using Kubernetes, Pulumi, and Terraform
Scale GPU and CPU compute resources to support AI model training and inference workloads that grow unpredictably
Build monitoring, alerting, and observability tooling that catches issues before customers do, using tools like Prometheus, Grafana, or equivalent
The Impact You Will Have
Enable Engineers to deploy models that reduce simulation time from hours to minutes, directly accelerating product innovation for Synopsys customers
Scale the platform to handle growing customer demand without degrading performance or reliability
Reduce mean t
📌 R&D Engineering, Staff Engineer (Madrid)
🏢 Ansys
📍 Madrid