About the job
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, knowledge 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 LLMs, VLMs, and multi-modality 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.
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.
Construct and curate large problem specific datasets for post-training, finetuning, and reinforcement learning.
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.
Drive continuous innovation in deep learning inference performance to strengthen NVIDIA platform integration and expand market adoption across the AI inference ecosystem.
Qualifications
Minimum
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.
Preferred
Contributions to PyTorch, Megatron-LM, NeMo, TensorRT-LLM, vLLM, SGLang, or other machine learning training and inference frameworks.
Hands-on training, fine-tuning, or reinforcement learning experience on LLM or VLM models with 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., NeMo, TensorRT, TensorRT-LLM).