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, Deep Learning, Semiconductors, Autonomous VehiclesIndustries

Requirements

BE (MS preferred) or equivalent experience in EE/CS with 10+ years of work experience
Deep practical knowledge of Large Language Models (LLMs), Machine Learning (ML), and Agent development
Strong background in implementing AI solutions to solve real-world software engineering problems
Hands-on experience with Python/Java/Go, with extensive Python scripting experience
Experience working with SQL/NoSQL database systems such as MySQL, MongoDB, or Elasticsearch
Full-stack, end-to-end development expertise, with proficiency in building and integrating solutions from front-end (e.g., React, Angular) to back-end (Python, Go, Java) and managing data infrastructure (SQL/NoSQL)
Experience with CI/CD tools such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef, or similar
Good understanding of distributed systems, microservice architecture, and REST APIs
Ability to effectively work across organizational boundaries to enhance alignment and productivity between teams
Ways to stand out
Proven expertise in applied AI, particularly using Retrieval-Augmented Generation (RAG) and fine-tuning LLMs on enterprise data to solve complex software engineering challenges
Experience delivering large-scale, service-oriented software projects under real-time constraints, demonstrating understanding of complex development environments
Expertise in leveraging LLMs and Agentic AI to automate complex workflows, with knowledge of RAG and fine-tuning LLMs on enterprise data

Responsibilities

Develop and implement solutions throughout software development lifecycles to improve developer efficiency, accelerate feedback loops, and boost release reliability
Design, develop, and deploy AI agents to automate software development workflows and processes
Continuously measure and report on the impact of AI interventions, showing progress in metrics such as cycle time, change failure rate, and mean time to recovery (MTTR)
Build and deploy predictive models to identify high-risk commits, forecast potential build failures, and flag changes with high probability of failures
Research emerging AI technologies and engineering best practices to continuously evolve the development ecosystem and maintain a competitive edge

Skills

Large Language Models

AI Agents

Agentic AI

Predictive Models

Software Development Lifecycle

DevOps Metrics

Cycle Time

MTTR

Change Failure Rate

Build Forecasting

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