Senior Deep Learning Engineer - Genomics at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Genomics, Healthcare, AIIndustries

Requirements

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
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

Responsibilities

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

Skills

Deep Learning

C/C++

Python

LLMs

Graph Neural Networks

Graph Transformer Networks

Multi-modal Models

DNA Sequencing

Variant Calling

Machine Learning

Bioinformatics

Computational Biology

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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