NVIDIA
AI Networking
SeniorSoftwareEngineer,AINetworking
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Software Engineer, AI Networking at NVIDIA. Skills: Machine Learning, Deep Learning, LLM training and inference, optimization techniques, networking, Python, C++. building and productizing machine learning tools. design and implement resource allocation and combinatorial optimization techniques”
What You'll Achieve.
ensuring the most efficient and productive utilization of system resources at data center scale; deliver valuable performance analysis insights; drive efforts to achieve those performance goals
Industry & Context.
optimization challenges; performance analysis; performance optimization
What They're Looking For.
Must Have
4+ years of experience applying machine learning techniques to computer architecture and system optimization problems, Hands-on experience developing and deploying various learning algorithms (e.g., reinforcement learning, offline RL, supervised learning) to tackle optimization challenges within computer architecture, system design, or networking domains, Proficiency in building and using ML models with leading frameworks such as PyTorch or TensorFlow, or JAX, Proven ability to apply GNNs/transformers-based optimization to PyTorch model graph and Kineto execution traces, Expertise combining knowledge of NVIDIA GPUs, the CUDA library, and deep learning frameworks (TensorFlow/PyTorch) with networking concepts, including collective communication libraries (like NCCL) and protocols (such as RoCE and RDMA), programming capabilities in Python, Bash, and C++
Nice to Have
In-depth knowledge and experience with machine learning/reinforcement learning and frameworks, Comprehensive understanding of computer architecture, system architecture and networking, Extensive experience in applying machine learning techniques such as GNNs or related graph-based models, Knowledge in PyTorch, CUDA, and NCCL libraries, Proven software engineering/development skills, ML-based combinatorial optimization and build space exploration (DSE) techniques, distributed Deep Learning, particularly within LLM training and inference stacks, collective communication and networking, learning-based agentic techniques, Experience applying machine learning techniques to computer architecture and system optimization problems, Desired experience involves bringing to bear ML at the intersection of at least two of the following areas: HPC, networking, and AI applications
What You'll Do.
building and productizing machine learning tools
design and implement resource allocation and combinatorial optimization techniques
and deploy AI/ML techniques to optimize large-scale Deep Learning (LLM) training and inference
Build and productionize ML-based tools for performance prediction and optimization
Develop and deploy a scalable
reliable data curation pipeline
Lead performance test planning
establish performance targets for new technologies and solutions
and drive efforts to achieve those performance goals
How You'll Work.
Team & Collaboration
Collaborate across hardware and software teams to deliver valuable performance analysis insights
Communication Scope
effective communication
Full Job Description
NVIDIA seeks a senior software engineer to join the AI Networking co-design and benchmark R&D team. In this pivotal role, the candidate is responsible for building and productizing machine learning tools. These include tools that use ML-based combinatorial optimization and build space exploration (DSE) techniques. These tools will be employed to optimize AI workloads across large GPU and CPU clusters, thereby ensuring the most efficient and productive utilization of system resources at data center scale. The role involves working on distributed Deep Learning, particularly within LLM training and inference stacks. A strong passion for collective communication and networking is desirable. The candidate will interact with diverse hardware and platforms, such as Host Channel Adapters (HCAs), Switches, CPUs, GPUs, and complete Systems. Furthermore, the role requires engagement across multiple software layers, including LLM applications, machine learning frameworks, and communication and computing libraries. The candidate will develop tools and methodologies using Machine Learning (ML) for comprehensive performance analysis and optimization, potentially incorporating learning-based agentic techniques. This work involves deep-diving across the software stack, from LLM applications and ML frameworks down to communication and computing libraries. This position offers a distinct opportunity to support the core infrastructure powering the next generation of large-scale AI systems. **What you 'll be doing:** * Design and implement resource allocation and combinatorial optimization techniques (e.g., reinforcement learning, LLM agents for DSE, Bayesian optimization and other multi-objective optimization techniques) to optimize LLM models at datacenter scale. * Research, develop, and deploy AI/ML techniques to optimize large-scale Deep Learning (LLM) training and inference on NVIDIA supercomputers and distributed systems. This includes a focus on high-performance networking and NV
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