NVIDIA

Senior Generative AI Research Engineer

California, United States

Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Artificial Intelligence, Computer Hardware, Software DevelopmentIndustries

Requirements

Candidates must have a minimum of 8 years of industry experience or 5+ years of research/postdoc experience in building and deploying generative AI systems. Proficiency in PyTorch, JAX, or other deep learning frameworks is essential. Expertise in one or more generative AI areas such as LLMs, coding agents, diffusion models, autoregressive models, VAE/GAN architectures, retrieval-augmented generation, neural rendering, or multi-agent systems is required. Familiarity with transformer architectures and attention mechanisms is necessary, along with hands-on experience in large-scale training (e.g., ZeRO, DDP, FSDP, TP, CP) and data processing (e.g., Ray, Spark). Production-quality software engineering skills in Python are highly relevant. An MS or PhD or equivalent experience in Computer Science, Machine Learning, Applied Math, Physics, or a related field, and 12+ years of relevant software development experience are needed. Familiarity with high-performance computing, GPU acceleration, influential open-source libraries or publications, multimodal data, and NVIDIA GPU-based compute clusters or simulation environments are preferred.

Responsibilities

The Senior Generative AI Research Engineer will design, post-train, and optimize foundation models for real-world applications. They will contribute to collaborative development of large-scale training infrastructure, high-efficiency inference pipelines, and scalable data pipelines. Responsibilities include working with research, software, and product teams to deploy world models from idea to production, collaborating on open-source and internal projects, authoring technical papers or patents, and mentoring junior engineers. The role involves rapid prototyping and iteration on experiments across cutting-edge AI domains such as agentic systems, reinforcement learning, reasoning, and video generation. Additionally, they will design and implement model distillation algorithms for size reduction and diffusion step optimization, and profile and benchmark training and inference pipelines to meet production-ready performance requirements.

Skills

Generative AI
Multimodal Learning
Video Generation
Synthetic Data
Intelligent Simulation
Agentic Systems
Foundation Models
LLMs
Diffusion Models
VLMs
VLAs
PyTorch
JAX
Deep Learning Frameworks
Large-scale Training Infrastructure
High-efficiency Inference Pipelines
Scalable Data Pipelines
Model Distillation
Transformer Architectures
Attention Mechanisms
Coding Agents
Autoregressive Models
VAE/GAN Architectures
Retrieval-Augmented Generation
Neural Rendering
Multi-agent Systems
Reinforcement Learning
Reasoning

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters
1993Year Founded
$19.5MTotal Funding
IPOCompany Stage
Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries
10,001+Employees

Benefits

Company Equity
401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.
Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.
Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.
The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.
NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.
Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.
Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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