Machine Learning Engineer, LLM Training Datasets at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

Requirements

Master’s or PhD in Computer Science, Electrical Engineering or related field - or equivalent experience
3+ years of work experience in developing datasets and training large language models or other generative AI models
Hands-on programming expertise in Python
Solid understanding of machine learning concepts and algorithms for managing data and experiments, including multi-modal datasets
Experience with synthetic data generation techniques, and evaluation strategies
Background with high-performance data processing libraries and machine learning frameworks like PyTorch, Data Loader, TensorFlow Data
Experience with alignment/fine-tuning of LLMs, VLMs (img-to-text, vid-to-text) or any-to-text large models
Familiarity with distributed training paradigms and optimization techniques
Good at problem solving and analytical ability as well as excellent collaboration and communication skills
Demonstrates behaviors that build trust: humility, transparency, respect, intellectual integrity

Responsibilities

Develop datasets for LLM pre-training and post training (fine-tuning and reinforcement learning), optimize models and evaluate performance
Design and implement data strategies for model training and evaluation that includes data collection, cleaning, labeling, augmentation, RL verifier datasets to improve model performance
Actively identify and manage data issues such as outliers, noise, and biases
Generate high-quality synthetic data to augment existing datasets, especially for domain-specific or safety-critical use cases and multi-modal use cases (text, image, video, etc)
Define data annotation guidelines and curate high-quality labeled datasets for model alignment, including reinforcement learning with human feedback (RLHF)
Conduct experiments to optimize Large Language Models with SFT and RL techniques
Design and develop systems for reasoning, tool calling, multi-modal processing, RL verifiers
Implement post-training tasks for LLMs, including fine-tuning, RL, distillation, and performance evaluation, and adjust hyperparameters to improve model quality
Partner with ML researchers, data scientists, and infrastructure teams to understand data needs, integrate datasets, and deploy efficient ML workflows

Skills

Machine Learning

LLM Training

Dataset Engineering

RLHF

SFT

Fine-tuning

Reinforcement Learning

Data Annotation

Synthetic Data Generation

Multi-modal Processing

Data Cleaning

Data Augmentation

Data Curation

Hyperparameter Tuning

RL Verifiers

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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