Senior DL Inference Architect (Distributed AI) - Equity

Nvidia Corporation in; Santa Clara (CA); Hybrid; USD 152,000 - 288,000; Full time

Job summary

NVIDIA is seeking a Senior Deep Learning Algorithms Engineer to advance Dynamo, our open-source distributed inference platform for large-scale, low-latency AI services. You’ll lead architecture and performance work across Dynamo and open source frameworks, collaborating with research, software, systems, and hardware teams to accelerate AI inference.

You’ll engage with communities like vLLM, SGLang, and TensorRT-LLM, push benchmarks, optimize runtimes and scheduling, and deliver measurable

Qualifications

• BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience).
• 3+ years building, profiling, and debugging performance-critical distributed or ML systems.
• Strong programming skills in Python and/or Rust, C++.
• Understanding of modern ML architectures and inference techniques.

Responsibilities

• Design, build, and maintain Dynamo integrations for open source frameworks vLLM, SGLang, TRTLLM.
• Partner with open source communities to land measurable gains in latency, throughput, reliability, and efficiency.
• Showcase NVIDIA token/watt leadership by pushing the pareto frontier on public/private benchmarks
• Find and remove bottlenecks across runtimes, kernels, networking, routing, and orchestration.
• Develop inference optimizations for scheduling, disaggregation, KV caching, and autoscaling.

Skills

Python

Rust

C++

Distributed systems

ML inference

Education

BS/MS/PhD in CS/EE/CE

Tools

TensorRT

vLLM

SGLang

TRTLLM

Profiling tools

Job description

NVIDIA is seeking a Senior Deep Learning Algorithms Engineer to advance Dynamo, our open-source distributed inference platform for large-scale, low-latency AI services. You’ll lead architecture and performance work across Dynamo and open source frameworks, collaborating with research, software, systems, and hardware teams to accelerate AI inference.

You’ll engage with communities like vLLM, SGLang, and TensorRT-LLM, push benchmarks, optimize runtimes and scheduling, and deliver measurable