Senior AI Workflow Engineer at NVIDIA

Santa Clara, California, United States

Apply Now

Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Semiconductors, AutomotiveIndustries

Requirements

BE (MS preferred) or equivalent experience in EE/CS with 10+ years of work experience
Well versed with Large Language Models (LLM), Machine Learning (ML), Agentic AI techniques
Hands-on experience in using large language models (LLMs) and implementing AI for software engineering workflows
Hands-on experience on Python/Java/Go with extensive Python scripting experience
Experience in working with SQL/NoSQL database systems such as MySQL, MongoDB or Elasticsearch
Experience in Full stack development. Proficient in front-end (e.g., React, Angular, Vue.js, HTML, CSS, JavaScript), back-end (e.g., Node.js, Python/Django/Flask, Ruby on Rails, Java/Spring, .NET) development, database management (SQL/NoSQL), and deployment/hosting (e.g., AWS, Azure, GCP)
Experience with tools for CI/CD setup such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef or similar tools
Good understanding of distributed systems, understanding of microservice architecture and REST APIs
Good to have knowledge of build tools like Make, Maven or Ant
Ability to effectively work across organizational boundaries to enhance alignment and productivity between teams
Expertise in leveraging large language models (LLMs) and Agentic AI to automate complex workflows, with knowledge of retrieval-augmented generation (RAG) and fine-tuning LLMs on enterprise data (ways to stand out)

Responsibilities

Design and implement AI-driven optimizations within software development workflows to enhance developer productivity, accelerate feedback loops, and improve release reliability
Experience designing, developing, and deploying AI agents to automate software development workflows and processes
Continuously measure and report on the impact of AI interventions, demonstrating improvements in key metrics like cycle time, change failure rate, and mean time to recovery (MTTR)
Create and deploy predictive models to identify high-risk commits, forecast potential build failures, and flag changes that have a high probability of failures
Conduct research on emerging technologies to recommend best practices and improvements
Proactively track AI tool and technology trends, build insights, and collaborate with development teams early to evangelize AI driven workflows NVIDIA adoption (ways to stand out)

Skills

Large Language Models

Agentic AI

AI Agents

Predictive Models

Software Development Workflows

DevOps Metrics

GPU

Deep Learning

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.

Land your dream remote job 3x faster with AI

Try Jobo Free