Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Senior CAD Semiconductor Engineer
- Posted
- 18D AGO
About the role
Country: Greece
Brand: Capgemini Engineering
Reference: 527569-en_GB
Contract type: Permanent
Experience level: Experienced Professionals
Professional community: Products & Systems Engineering
Business unit: Engineering and RandD Services
Technical Skills
EDA / CAD Flow Expertise
Strong experience with full EDA flows
- Simulation, synthesis, place & route, verification, DFT flows
- Tools from Cadence, Synopsys, Siemens EDA (Mentor Graphics)
- Tool validation, qualification, and deployment
Strong experience with
- EDA licensing systems (FlexLM/Flexera)
- Tool versioning, release management, and regression validation
Good understanding of
- RTL design, verification flows, and physical design constraints
- Interaction between tools and silicon quality/productivity
- Knowledge in ISO26262, DO-254 is a plus
Infrastructure & Automation
Strong experience in
- Linux-based compute farms for EDA workloads
- Distributed storage systems (NFS, parallel/distributed FS)
- CI/CD systems (Jenkins, GitLab CI or equivalent)
Scripting and automation
- Python, Perl, Shell, TCL
- Infrastructure-as-code and automation mindset
Experience with
- Monitoring and performance optimization
- Backup, disaster recovery, high availability
AI / Modern Engineering Tooling
Practical experience with
- AI/LLM-based tooling integration (e.g., Copilot, OpenAI, Gemini, Mistral)
- Hybrid AI infrastructure (cloud + on-prem GPU environments)
- Use cases such as
- Workflow automation
- Debug assistance
- Knowledge retrieval (RAG)
- Engineering productivity optimization
Additional skills
Object-oriented programming concepts
Familiarity with
- Version control systems (Git, SVN, etc.)
- DevOps practices and toolchains
Mission
Define, deploy, and maintain EDA tool flows and CAD infrastructure supporting semiconductor design and verification activities across full SoC lifecycle.
Drive the evaluation, benchmarking, and integration of EDA tools (Cadence, Synopsys, Siemens EDA, Arteris) and associated flows to maximize productivity, reliability, and scalability.
Own the engineering infrastructure environment (compute, storage, licensing, CI/CD, automation) required for advanced chip design workloads.
Collaborate with design, verification, and physical implementation teams to ensure efficient, robust, and scalable tool usage.
Explore and integrate AI/ML-based tooling and workflows to improve engineering productivity (automation, debugging, optimization, knowledge extraction).
Ensure optimal trade-offs between performance, cost, scalability, and usability of the CAD environment.
Act as a key technical interface between internal engineering teams, management, and EDA vendors.
Provide regular reporting on tool performance, usage, and roadmap alignment.
Main Responsibilities
Define, deploy, and maintain EDA flows for digital, mixed-signal, and verification activities (simulation, synthesis, P&R, DFT, signoff).
Evaluate, benchmark, and qualify new EDA tools, releases, and methodologies before production deployment.
Design and operate EDA infrastructure environments, including
- Compute farms (Linux-based clusters for simulation and regression)
- Storage systems (NFS, distributed storage, backup/DR)
- License servers (FlexLM/Flexera)
Develop and maintain automation frameworks for
- Tool installation and configuration
- Regression and CI/CD pipelines
- Resource provisioning and monitoring
Implement and manage CI/CD pipelines (e.g., Jenkins, GitLab CI) for design and verification workflows.
Collaborate with engineering teams to
- Optimize tool usage and flows
- Debug tool-related issues
- Improve turnaround time and efficiency
Maintain strong interaction with EDA vendors (Cadence, Synopsys, Siemens)
- Tool support, issue resolution, roadmap alignment
- Licensing strategy and capacity planning
Define and enforce best practices and standards for CAD usage and tool flows.
Integrate AI-driven capabilities into engineering workflows, including
- LLM-assisted scripting and automation
- AI-based log analysis, debug assistance, and workflow optimization
- Hybrid cloud/on-prem AI infrastructure for sensitive data
Support security, compliance, and reliability requirements (e.g., ISO 27001 environments).
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