Machine Learning Engineer - Distributed AI & GPU Pipelines

NVIDIA Corporation; Santa Clara (CA); On-site; USD 152,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a talented Machine Learning Engineer to drive development, evaluation, deployment and end-to-end lifecycle management of AI-powered systems. You will apply AI agents, build automated testing frameworks, and implement secure CI/CD pipelines with GitLab to ensure code quality and system resilience.

A core component is deploying and scaling models across distributed infrastructure, managing GPU orchestration, and building advanced AI workflows with Kubernetes, Ray, or Slurm.

Qualifications

• Master's or PhD in CS/EE or related field or equivalent experience.
• 3+ years of professional experience writing production-grade, asynchronous Python.
• Deep experience with LangChain, Hugging Face libraries, vLLM, and SGLang.
• Experience with ML frameworks like TensorFlow, PyTorch and Scikit-learn.
• Data analysis in Python (pandas/NumPy) to communicate results clearly.
• Deployment and orchestration with Kubernetes, Ray, or Slurm.

Responsibilities

• Architect, deploy, and scale open-source models using Kubernetes, Ray, or Slurm.
• Design ML systems and data pipelines; evaluate and deploy production models and agents.
• Run benchmarks and perform error and gap analysis; build performance dashboards.
• Own features from ideation to production across multiple repositories.

Skills

Python

Kubernetes

Ray

Slurm

GitLab CI/CD

PyTorch

Education

Master's or PhD in CS/EE or related

Tools

LangChain

Hugging Face

vLLM

SGLang

TensorFlow

PyTorch

Job description

NVIDIA is seeking a talented Machine Learning Engineer to drive development, evaluation, deployment and end-to-end lifecycle management of AI-powered systems. You will apply AI agents, build automated testing frameworks, and implement secure CI/CD pipelines with GitLab to ensure code quality and system resilience.

A core component is deploying and scaling models across distributed infrastructure, managing GPU orchestration, and building advanced AI workflows with Kubernetes, Ray, or Slurm.