NVIDIA is seeking a senior software engineer to join its AI Networking co-design and benchmark R&D team, where the candidate will design and implement machine learning tools for optimizing large-scale deep learning training and inference across GPU and CPU clusters. This involves developing resource allocation techniques using reinforcement learning and other optimization methods, building scalable data curation pipelines, and collaborating with hardware teams to deliver performance analysis insights. The role requires expertise in PyTorch or TensorFlow, proficiency in Python, Bash, and C++, and a deep understanding of NVIDIA GPUs, CUDA, and networking concepts like NCCL and RDMA protocols. Ideal candidates have 4+ years of experience applying ML techniques to computer architecture optimization problems at the intersection of HPC, networking, and AI applications.
Skills
Python
PyTorch
TensorFlow
JAX
CUDA
NCCL
Reinforcement_Learning
Bayesian_Optimization
GNNs
Docker
Kubernetes
CI/CD
Prometheus
Grafana
Bash
C++
PostgreSQL
Redis
What you'll do
Design and implement resource allocation and combinatorial optimization techniques for LLM models at datacenter scale.
Research and develop AI/ML techniques to optimize large-scale Deep Learning training and inference on NVIDIA supercomputers.
Build and productionize ML-based tools for performance prediction and optimization with a focus on networking aspects.
Develop scalable, reliable data curation pipelines to support the training of high-performance Machine Learning models.
Lead performance test planning and establish performance targets for new technologies in distributed systems.
Collaborate across hardware and software teams to deliver valuable performance analysis insights.
What we're looking for
4+ years of experience applying ML to computer architecture and system optimization problems.
Hands-on experience with reinforcement learning, supervised learning, and other ML algorithms for optimization challenges.
Proficiency in PyTorch or TensorFlow for building and deploying ML models.
Expertise combining knowledge of NVIDIA GPUs, CUDA, deep learning frameworks, and networking concepts.
Strong programming skills in Python, Bash, and C++.
Proven ability to apply GNNs/transformers-based optimization to PyTorch model graphs and execution traces.
Market check
Salary context
This
$152,000–$241,500
range sits above
54%
of similar postings on FindRole.
Peer median band
$150,000–$236,675
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$158,375–$235,750
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
801 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 797 roles with salary data.
Remote (Us, Ca, Santa Clara, US)10 days ago
$224,000–$356,500
C
C++
Rust
RDMA
GPUDirect
NVLink
InfiniBand
RoCE
GPU
DPU
NIC
switch
vLLM
SGLang
TensorRT-LLM
NVMe-oF
GPUDirect Storage
S3
Reinforcement Learning
ML inference frameworks