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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI systems architecture, including deep learning frameworks and performance optimization techniques. Proven ability to lead architectural development and enhance GPU networking offerings for accelerated AI workloads.
Highest-signal resume keywords
AI Systems ArchitectureDeep Learning FrameworksPerformance Profiling and OptimizationSystem ProgrammingVirtualization and Networking
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Algorithm DesignSystem ArchitectureDeep Learning Training WorkloadsPerformance Optimization TechniquesProgramming Skills
Soft Skills
Stellar Communication SkillsAbility to Work in Multi-National Environments
Tools & Technologies
NVIDIA DynamoNVIDIA NIXLNVIDIA UCXNCCLMPI
Certifications & Qualifications
B.Sc.M.Sc.Ph.D.
Industry Keywords
High Performance ComputingSoftware Defined NetworkingData AnalyticsParallelism of AI FrameworksInference and Training Workloads
About the role
Key responsibilities & impact- Explore technological challenges in accelerated networking and building AI data centers
- Research new transport functions and semantics for optimizing AI workloads, AI systems communication and accelerations
- Lead architectural and development efforts across distributed AI and deep learning solutions, data analytics, High Performance Computing (HPC), Software Defined Networking (SDN), virtualization and storage
- Enhance NVIDIA’s GPU Networking offerings for accelerating AI workloads, such as NVIDIA Dynamo, NVIDIA NIXL and NVIDIA UCX
- Co-design hardware features in GPUs, DPUs or interconnects to accelerate data movement and enable new inference and model-serving capabilities
- Identify and evaluate new technologies, innovations and partner relationships for alignment with NVIDIA’s technology roadmap and business value
- Lead architecture and design of runtime systems, communication libraries and AI-specific technologies
- Lead proof-of-concept development to evaluate and drive technologies
Requirements
What you’ll need- Hold a B.Sc., M.Sc. or Ph.D. in Computer Science, Electrical or Computer Engineering from a leading university (or equivalent experience)
- 5+ years of industry experience (or equivalent) in system architecture, AI systems architecture, scaling of AI, parallelism of AI frameworks, or deep learning training workloads
- Experienced in algorithm design, system programming, computer architecture and operating systems
- Experienced in virtualization, networking and storage
- Deep understanding of performance profiling and optimization techniques, together with defining and using hardware features
- Strong programming and software development skills
- Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment
- Shown research track record
- Experience and passion for system architecture, CPU/GPU/memory/storage/networking
- Stellar communication skills
- Knowledge in Deep Learning frameworks and AI communication libraries (NCCL, UCX, MPI and equivalents)
- Deep understanding of Inference and Training workloads and optimizations, like Prefill/Decode, data parallelism, Tensor parallelism, FSDP and others