[Hiring] IT & Infrastructure Architect @TYLsemi

Role Description

We are looking for a hands-on and highly strategic IT & Infrastructure Admin to build and manage the end-to-end compute, storage, network, and EDA infrastructure required for designing complex SoCs across digital and analog domains. This role goes beyond traditional IT—it requires deep ownership of EDA environments, compute strategy (cloud vs on-prem), cost optimization, and AI infrastructure enablement, ensuring high performance, scalability, and reliability for engineering teams.

Key Responsibilities

• EDA & Engineering Infrastructure
  • Own setup, deployment, and management of EDA tools and environments for digital design and verification, and analog and custom design flows.
  • Manage tool installations, upgrades, and compatibility across flows.
  • Drive EDA license management, including forecasting demand across teams and projects, optimizing utilization and cost, and vendor coordination and negotiations.
  • Ensure high availability and performance of compute farms and storage systems.
• Compute & Platform Strategy
  • Define and execute strategy for cloud vs on-prem infrastructure.
  • Evaluate AWS (or other cloud platforms) vs owned/rented servers.
  • Build cost models and ROI analysis for different scaling scenarios.
  • Design scalable infrastructure for large regressions (DV workloads), RTL synthesis and physical design, and analog simulations (compute-intensive workloads).
  • Optimize job scheduling, workload distribution, and resource utilization.
• Network & Systems Management
  • Design and manage high-performance network infrastructure for low-latency, high-throughput connectivity for EDA workloads.
  • Secure remote access for distributed teams.
  • Manage servers, storage (NAS/SAN), and backup systems.
  • Manage OS environments (primarily Linux-based).
  • Oversee data security, access control, and disaster recovery.
• AI Infrastructure & Enablement
  • Support deployment and scaling of AI/ML infrastructure for engineering workflows.
  • Work with AI and engineering teams to enable AI agent workflows and optimize compute usage (GPU/CPU allocation).
  • Define and enforce AI usage guardrails, including data security and IP protection, and safe usage policies for internal and external AI tools.
  • Manage token usage, cost tracking, and access control for AI platforms.
• Planning, Forecasting & Cost Optimization
  • Develop and maintain forecasts for compute infrastructure (cloud + on-prem), EDA licenses, and storage and network capacity.
  • Continuously optimize for cost vs performance vs scalability trade-offs.
  • Provide leadership with data-driven recommendations on infrastructure investments.

Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field.
• 10+ years of experience in IT infrastructure / systems engineering, preferably in semiconductor or EDA environments.
• Strong experience with EDA tool environments (Synopsys, Cadence, Siemens/Mentor).
• Linux system administration.
• Compute cluster management and job schedulers (LSF, Slurm, etc.).
• Experience managing large-scale compute and storage systems.
• Strong understanding of networking fundamentals (high-performance networks preferred).
• Experience with cloud platforms (AWS preferred).

Preferred Qualifications

• Experience supporting SoC design teams (RTL, DV, Analog).
• Familiarity with analog simulation environments and their compute demands.
• Experience with hybrid cloud architectures.
• Exposure to GPU infrastructure and AI/ML workloads.
• Scripting skills (Python, Bash, etc.) for automation.
• Experience with security and compliance in IP-sensitive environments.

Key Attributes

• Strong ownership and end-to-end accountability mindset.
• Ability to balance technical depth with strategic decision-making.
• Bias toward automation, scalability, and efficiency.
• Strong problem-solving and operational excellence.
• Comfortable working in a fast-paced startup environment.

Success Metrics

• Reliable, scalable infrastructure supporting high engineering productivity.
• Optimized EDA license utilization and cost efficiency.
• Effective cloud vs on-prem strategy with measurable ROI.
• Minimal downtime and high system availability.
• Secure and efficient AI infrastructure adoption.
• Ability to scale infrastructure seamlessly with company growth.