Full-Time

Deep Learning Scientist

LLM Training Datasets

Posted on 10/31/2025

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$148k - $287.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Remote in USA + 2 more

More locations: California, USA | Santa Clara, CA, USA

Remote

Category
AI & Machine Learning (2)
,
Required Skills
LLM
Python
Data Science
Tensorflow
Pytorch
Machine Learning
Data Analysis
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
Desired Qualifications
  • Strong track record of contributions to open-source data tools or research publications
  • Experience with cloud platforms (e.g., AWS, GCP, Azure) and data storage systems (e.g., S3, Google Cloud Storage)
  • Stay ahead of research: Continuously evaluate new tools, techniques, and methodologies in data engineering and generative AI to improve training data infrastructure
  • Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research and publication experience

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Simplify Jobs

Simplify's Take

What believers are saying

  • Data centers generate 89% of $215.9B FY2026 revenue.
  • $40B acquisitions like OpenAI bolster AI infrastructure dominance.
  • Nemotron models and Drive Thor accelerate agentic AI adoption.

What critics are saying

  • AMD MI450X outperforms Blackwell by 25% per watt in inference.
  • Huawei Ascend 910D blocks $10B China AI sales due to bans.
  • Google TPU v6 cuts hyperscaler GPU dependency by 60%.

What makes NVIDIA unique

  • NVIDIA invented GPU in 1999, pioneering accelerated computing.
  • CUDA platform from 2006 enables GPUs for AI and HPC.
  • Holds 92% discrete GPU market share as of Q1 2025.

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Company Equity

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Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

-3%

2 year growth

-2%
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