63 applicants

Company

NVIDIA · 18 hours ago

Senior System Software Engineer - Scientific Computing PaaS

California, United States

Full-time

Remote

Senior Level, Lead/Staff

$180K/yr - $339K/yr

10+ years exp

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Artificial Intelligence (AI)GPU

Growth Opportunities

H1B Sponsor Likely

Hiring Manager

Bella Yanovsky

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Responsibilities

Design, Build, Deploy and Operate Cloud native microservices and APIs for scientific computing workload on DGX cloud.

Design services and take ownership of underlying cloud infrastructure for physics informed and data driven scientific workflows

Design novel algorithms and actively engaged with operations to increase overall system performance, it spans across the stack e.g. deep understanding of application code e.g DL Framework, Numerical Solvers, Microservices, APIs and Heterogeneous accelerated computing with CPUs and GPUs.

Design, Build, Deploy and Operate scalable I/O infrastructure for checkpointing, data loading, pre & post processing of data.

Optimize compute, storage and network architecture specific to physics & simulation driven applications.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Cloud ComputingDistributed SystemsParallel ProcessingMicroservicesNvidia DGX CloudCompiled LanguagesAlgorithmic ThinkingSystem DesignAI PlatformsHPC ClustersCloud Native SystemsPerformance Optimization

Required

BS/MS degree in Computer Science or related areas or equivalent experience.

10+ years experience working on building and operating distributed compute and data intensive platform as a service on cloud

Proven skill in a compiled language (Go, Rust, C++ or otherwise).

Strong foundational knowledge in Cloud Computing e.g “The Datacenter is a Computer” architecture, cloud security architecture, virtualization - CPU, Memory and IO, Resource pooling and elasticity.

Proven skills in Distributed Systems & Parallel Processing e.g System model of distributed computation e.g. topology abstraction, logical time. Synchronization and deadlock detection in distributed systems, Fault Tolerance and Failure Detection, Consensus and Agreement protocols, Parallel algorithms, shared memory and distributed memory architecture, message passing (MPI, NCCL), Cluster scalability and performance.

Hands on Debugging skills with Process, Threads , Deadlock and Synchronization, Scheduling, IPC, Memory management, File system and I/O structure.

Strong Evidence on Algorithmic Thinking & System Design skills e.g Recursion, Graph, Tree, Stack and Queue, Large scale loosely coupled distributed system design and operational experience.

Be self-motivated, have strong interpersonal skills, and be able to work independently with multiple teams with minimal direction.

Preferred

Have built , deployed and operated AI platforms on HPC clusters.

Have built, deployed and operated cloud native system including distributed storage, scheduling, and orchestration among compute, storage and network.

Configuring and troubleshooting hardware, operating systems, kernel, compilers for maximum performance.

Hands on debugging skills to optimize performance of compute, networking and I/O framework.

Extensively worked on third party source code for debugging and customization.

Benefits

Equity and benefits

Company

NVIDIA

Glassdoor

4.6

NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI.

Founded in 1993

Santa Clara, California, USA

10,001+ employees

https://www.nvidia.com

H1B Sponsorship

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2020 (534)