Data Center Hardware Specialist II, Google Cloud (Fixed-Term Contract)
Description
US: $152000 - $221000 (USD) + 15% bonus target
Learn more about benefits at Google.
- Oversee the end-to-end deployment of accelerated compute clusters, serving as the lead technical advisor for confidential, enterprise infrastructure initiatives.
- Collaborate with customer technical leads and client executives to manage and deliver successful implementations of technical solutions for high-density computing environments.
- Partner with internal specialists, Product, and Engineering teams to package technical playbooks, best practices, and reference architectures for high-performance computing, influencing the roadmap for next-generation hardware and software integration.
- Engage with sales and partners to manage project scope, priorities, deliverables, risks and issues, and timelines for successful client outcomes.
- Travel up to 30% of the time for client engagements as needed.
Minimum qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, a related technical field, or equivalent practical experience.
- 5 years of experience in technical solution delivery and project management for enterprise infrastructure projects.
- 4 years of experience in technical architecture, systems administration, or infrastructure delivery in cloud or on-premises environments.
- Experience managing customer relationships and project stakeholders, including executive-level communication and influence on technical and operational decisions.
- Experience in hardware triage, diagnostics, and guiding repair workflows for server clusters and high-performance networks.
Preferred qualifications:
- Master's degree or PhD in Computer Science or Engineering, or a related field.
- Experience with end-to-end system architecture or accelerated computing hardware (e.g., Kubernetes, GKE, EKS, GPU workloads, or Linux-based distributed systems).
- Experience with networking and system design of load balancers, firewalls, and VPN in architecting, developing and maintaining production-grade systems.
- Experience in maintaining clusters of massive-scale model training using high-end hardware accelerators and AI/ML infrastructure frameworks such as JAX, PyTorch, or OpenXLA.





