Nvidia Machine Learning Engineer LLM Training Datasets 

United States, Texas 

742296262

NVIDIA is looking for a dedicated Machine Learning Engineer specializing in LLM training datasets engineering. This is a highly technical role requiring deep expertise in machine learning, data science and data engineering to develop innovative solutions that address the unique challenges of training foundation models. This role involves addressing innovative machine learning challenges through building and improving our data ecosystem.

What you'll be doing:

• 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.

What we need to see:

• You have a 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 withalignment/fine-tuningof 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.

Ways to stand out from the crowd:

• 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.