Nvidia Senior GenAI Algorithms Engineer — Model Optimizations Inference 

United States, Texas 

959483596

What you’ll be doing:

• Design and build modular, scalable model optimization software platforms that deliver exceptional user experiences while supporting diverse AI models and optimization techniques to drive widespread adoption.

• Explore, develop, and integrate innovative deep learning optimization algorithms (e.g., quantization, speculative decoding, sparsity) into NVIDIA's AI software stack, e.g., TensorRT Model Optimizer, NeMo/Megatron, and TensorRT-LLM.

• Deploy optimized models into leading OSS inference frameworks and contribute specialized APIs, model-level optimizations, and new features tailored to the latest NVIDIA hardware capabilities.

• Partner with NVIDIA teams to deliver model optimization solutions for customer use cases, ensuring optimal end-to-end workflows and balanced accuracy-performance trade-offs.

• Conduct deep GPU kernel-level profiling to identify and capitalize on hardware and software optimization opportunities (e.g., efficient attention kernels, KV cache optimization, parallelism strategies).

• Drive continuous innovation in deep learning inference performance to strengthen NVIDIA platform integration and expand market adoption across the AI inference ecosystem.

What we need to see:

• Master’s, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.

• 5+ years of relevant work or research experience in deep learning.

• Strong software design skills, including debugging, performance analysis, and test development.

• Proficiency in Python, PyTorch, and modern ML frameworks/tools.

• Proven foundation in algorithms and programming fundamentals.

• Strong written and verbal communication skills, with the ability to work both independently and collaboratively in a fast-paced environment.

Ways to stand out from the crowd:

• Contributions to PyTorch, JAX, vLLM, SGLang, or other machine learning training and inference frameworks.

• Hands-on experience training or fine-tuning generative AI models on large-scale GPU clusters.

• Proficient in GPU architectures and compilation stacks, adept at analyzing and debugging end-to-end performance.

• Familiarity with NVIDIA’s deep learning SDKs (e.g., TensorRT).

• Experience developing high-performance GPU kernels for machine learning workloads using CUDA, CUTLASS, or Triton.