Senior GenAI Algorithms Engineer — Model Optimizations for Inference

NVIDIA•Austin, CA

23h•$148,000 - $287,500

About The Position

NVIDIA is at the forefront of the generative AI revolution! The Algorithmic Model Optimization Team specifically focuses on optimizing generative AI models such as large language models (LLM) and diffusion models for maximal inference efficiency using techniques ranging from quantization, speculative decoding, sparsity, distillation, pruning to neural architecture search, and streamlined deployment strategies with open-sourced inference frameworks. Seeking a Senior Deep Learning Algorithms Engineer to improve innovative generative AI models like LLMs, VLMs, multimodal and diffusion models. In this role, you will design, implement, and productionize model optimization algorithms for inference and deployment on NVIDIA’s latest hardware platforms. The focus is on ease of use, compute and memory efficiency, and achieving the best accuracy–performance tradeoffs through software–hardware co-design. Your work will span multiple layers of the AI software stack—ranging from algorithm design to integration—within NVIDIA’s ecosystem (TensorRT Model Optimizer, NeMo/Megatron, TensorRT-LLM) and open-source frameworks (PyTorch, Hugging Face, vLLM, SGLang). You may also dive deeper into GPU-level optimization, including custom kernel development with CUDA and Triton. This role offers a unique opportunity to work at the intersection of research and engineering, pushing the boundaries of large-scale AI optimization. We are looking for passionate engineers with strong foundations in both machine learning and software systems/architecture who are eager to make a broad impact across the AI stack.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.