Senior Research Scientist, Post-Training LLM and DLM at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

Requirements

PhD in Computer Science, Electrical Engineering, or related field, or equivalent research experience in LLMs, systems, or related areas
2+ years of experience in machine learning, systems, distributed computing, or large-scale model training
Proficiency in Python with hands-on experience in frameworks such as PyTorch
Solid background in computer science fundamentals: algorithms, data structures, parallel/distributed computing, and systems programming
Proven ability to collaborate across research and engineering teams in multifaceted environments

Responsibilities

Designing and implementing post-training algorithms for LLMs and DLMs
Driving efficiency and scalability improvements across training pipelines and serving systems
Collaborating with researchers to translate cutting-edge ideas into production-ready implementations
Exploring new paradigms for evaluation
Demonstrating strong engineering practices, and contributing to open-source communities

Skills

Key technologies and capabilities for this role

PythonPyTorchLLMsDLMspost-training algorithmsdistributed computinglarge-scale model trainingsystems programmingalgorithmsdata structuresparallel computingmachine learning

Questions & Answers

Common questions about this position

What is the salary range for this position?

The base salary range is 160,000 USD - 258,750 USD for Level 3, and 184,000 USD - 299,000 USD for Level 4, determined based on location, experience, and pay of employees in similar positions.

What benefits are offered for this role?

You will be eligible for equity and benefits in addition to base salary.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What skills and experience are required for this Senior Research Scientist role?

Required qualifications include a PhD in Computer Science, Electrical Engineering, or related field (or equivalent), 2+ years in machine learning, systems, distributed computing, or large-scale model training, proficiency in Python with PyTorch, and a solid background in algorithms, data structures, parallel/distributed computing, and systems programming.

What makes a candidate stand out for this position?

Candidates stand out with expertise in post-training LLMs with novel algorithmic/data pipelines, experience developing and scaling large distributed systems for deep learning, and contributions to open-source LLM systems or large-scale AI infrastructure.

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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