FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in GPU asset provisioning, configuration, and lifecycle management across cloud providers, with a strong focus on building automated systems for large-scale Machine Learning environments. Proficient in systems programming, Linux administration, and container orchestration, ensuring reliability and scalability in distributed systems.
Highest-signal resume keywords
GPU Asset ProvisioningSystems Programming (Go, Python, Rust)Linux System AdministrationContainer Orchestration (Kubernetes, SLURM)Distributed Systems Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data StructuresAlgorithmsCluster Management SystemsRDMA NetworkingDistributed Network Design
Tools & Technologies
KubernetesSLURMMachine Learning SystemsData Center Management
Industry Keywords
Cloud ProvidersData SynchronizationFault ToleranceState ManagementPerformance Security
Tech Stack
Tools & technologiesCloudDistributed SystemsKubernetesLinuxPythonRustGo
About the role
Key responsibilities & impact- Contribute to a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers
- Build end-to-end automation of data center management, break/fix, and the full span of system upkeep for large-scale Machine Learning systems
- Implement monitoring and health management capabilities for reliability, availability, and scalability of GPU assets
- Harness data streams ranging from GPU hardware diagnostics to cluster and network telemetry
- Build automated test infrastructure to qualify distributed systems for operation
- Partner with engineering teams across NVIDIA to ensure software integrates from hardware through AI training applications
Requirements
What you’ll need- 5+ years of experience in a similar role working on large-scale production systems
- BS in Computer Science/Engineering/Physics/Mathematics or other comparable degree, or equivalent experience
- Expert level knowledge of a systems programming language (Go, Python, and/or Rust)
- Solid understanding of Data Structures and Algorithms
- Strong background in Linux system administration and management
- Deep knowledge of container orchestration platforms such as Kubernetes and/or SLURM
- Understanding of performance, security, and reliability in complex distributed systems
- Familiarity with system level architecture, data synchronization, fault tolerance, and state management
- Proficiency in designing and building widespread distributed networks
- Experience managing large-scale distributed systems independently of cloud providers
- Extensive expertise in datacenter operations and GPU hardware
- Hands-on experience working with RDMA networking
- Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM)
Benefits
Comp & perks- Equity
- Benefits 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score