Nvidia Senior Deep Learning Engineer - Genomics 

United States, Texas 

56622612

What you'll be doing:

• Develop and refine deep learning models and techniques for genomics analysis, including but not limited to DNA sequencing, variant calling, and model prediction.

• Advance and apply modern Deep Learning techniques to develop Large Language Models (LLMs), Graph Neural Networks, Graph Transformer Networks, and comprehensive multi-modal models in genomics.

• Design and implement machine learning techniques to tailor foundation models for downstream genomic specific tasks.

• Generate and manage datasets for large-scale machine learning, focusing on learning from genomics specific applications.

• Collaborate closely with product and hardware architecture teams to ensure flawless integration of research and development into NVIDIA products.

• Work in tandem with engineering and AI research teams to employ the latest technologies for scalable and innovative genomics analysis.

What we need to see:

• Master or Ph.D. in Computer Science, Bioinformatics, or Computational Biology, or related field (or equivalent experience).

• 3+ years of experience in related field.

• Proficiency in C/C++ and Python, with a strong grasp of software design and programming principles.

• Background with Large Language Models (LLMs) and natural language processing (NLP), Generative AI and Foundation Models.

• Strong proficiency with modern frameworks such as PyTorch and TensorFlow, Experience with Large scale inferencing.

• Experience in building and implementing complex algorithms and data structures, with a focus on bioinformatics or genomics applications.

• Deep understanding of computer system architecture, operating systems, and the challenges associated with large-scale genomic data analysis.

Ways to stand out from the Crowd:

• Hands-on experience in using LLM, Graph Neural Network, Graph Transformer Network particularly those applied to genomics data.

• Strong collaborative and interpersonal skills to effectively work and influence within a dynamic, technical environment.

• Ability to decompose complex requirements into step by step tasks and reuse available solutions to implement most of those.