• Develop, Train, Fine-tune, and Deploy multimodal large language models for retrieval augmented generation and tools usage
• Build LLM agent framework for reasoning and action prediction in multimodal environment
• Apply instruction tuning, reinforcement learning from human feedback (RLHF), and parameter efficient fine-tuning such as p-tuning, adaptors, Lo RA, and so on to improve LLMs for reasoning and action prediction
• Measure and benchmark model and application performance
• Analyze model accuracy and bias and recommend the next course of action & Improvements.
• Maintain model evaluation systems
• Drive the gathering, building, and annotation of domain specific datasets to train LLMs for different tasks, tools, and applications.
• Characterize performance and quality metrics across platforms for various AI and system components.
• Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.
• Help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment and collaborate with various teams on new product features and improvements of existing products.

• Master’s degree (or equivalent experience) or Ph D in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math with 5+ years of experience
• Understanding of LLM Agent development approaches for multi-step planning, reasoning, and tools interaction
• Hands-on experience with LLM agent frameworks including Open AI functions, Auto GPT, Baby AGI, and Plan-and-execute agents.
• Experience developing production LLM powered applications and tools with natural language interface
• Excellent programming skills in Python with strong fundamentals in programming, optimizations and software design
• Solid understanding of ML/DL techniques, algorithms and tools with exposure to CNN, RNN (LSTM), Transformers (BERT, BART, GPT/T5, Megatron, LLMs)
• Experience with training BERT, GPT and Megatron Models for different NLP and dialog system application using “Py Torch” Deep Learning Frameworks and performing NLP data wrangling and tokenization
• Understanding of MLOps life cycle and experience with MLOps workflows & traceability and versioning of datasets including knowhow of database management and queries (in SQL, Mongo DB etc)
• Experience using end-to-end MLOps platform such as Kube Flow, MLFlow, Air Flow
• Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment

• Fluency in a non-English language - Spanish / Mandarin / German / Japanese / Russian / French / UK English / Arabic/ Korean / Italian / Portuguese
• Familiarity with GPU based technologies like CUDA, Cu DNN and Tensor RT
• Background with Dockers and Kubernetes and Strong C++ programming skills
• Background with deploying machine learning models on data center, cloud, and embedded systems
• Experience developing document extraction for different documents types and sources, and indexing at scale as well as experience adapting LLMs to different domains such as automotive, health care, finance and so on

The base salary range is 148,000 USD - 276,000 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.