ML Engineer: Scalable AI Systems & GPU Orchestration

NVIDIA AI; 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 Machine Learning Engineer to drive end-to-end AI system development, deployment, and lifecycle management. You will deploy and scale models across distributed infrastructure, manage GPU orchestration, and build advanced AI workflows with Kubernetes, Ray, or Slurm.

You will design experiments, prompt-tune models, and benchmark performance while collaborating across teams to ensure robust, production-grade AI solutions.

Qualifications

• Master's/PhD or equivalent experience in CS/EE or related field.
• 3+ years producing production-grade asynchronous Python with clean architecture.
• Deep experience with LangChain, Hugging Face, vLLM, and SGLang.
• Data analysis in Python (pandas, NumPy) to derive insights.
• Production deployment and scaling with Kubernetes, Ray, or Slurm.

Responsibilities

• Architect, deploy, and scale models using Kubernetes, Ray, or Slurm for robust AI workloads.
• Design ML systems and data pipelines; prompt-tune and deploy production models and agents.
• Benchmark, analyze errors, and build dashboards to communicate performance to stakeholders.
• Own features from ideation to production across multiple repos and teams.

Skills

Python & Systems Engineering

AI Tools

Data Analysis

Deployment & Orchestration

Hardware & Scaling

GitLab CI/CD & Security

Testing Toolchains

Advanced Git Workflows

Education

Master's or PhD in CS/EE or related field

Tools

LangChain

Hugging Face

vLLM

SGLang

TensorFlow

PyTorch

Scikit-learn

Job description

NVIDIA is seeking a Machine Learning Engineer to drive end-to-end AI system development, deployment, and lifecycle management. You will deploy and scale models across distributed infrastructure, manage GPU orchestration, and build advanced AI workflows with Kubernetes, Ray, or Slurm.

You will design experiments, prompt-tune models, and benchmark performance while collaborating across teams to ensure robust, production-grade AI solutions.