ML Systems Engineer - Distributed AI & GPU

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

Benefits offered by this job

Equity

Benefits package

Job summary

NVIDIA is seeking a talented Machine Learning Engineer to drive end-to-end lifecycle management of AI-powered systems across distributed infrastructure. You will deploy and scale models, manage GPU orchestration, and build automated testing and CI/CD pipelines using GitLab.

The role demands strong Python engineering skills, experience with Kubernetes, Ray, Slurm, and ML frameworks, and a track record of delivering production-grade AI workflows at scale in a fast-paced environment.

Qualifications

• Master’s or PhD in Computer Science, Electrical Engineering, or related field, or equivalent experience.
• 3+ years of production-grade Python and distributed systems design.
• Experience with LangChain, Hugging Face, vLLM, and SGLang; TensorFlow, PyTorch, Scikit-learn.
• Proficient in Python data analysis with pandas/NumPy.
• Production-grade model deployment and multi-node scaling with Kubernetes, Ray, Slurm.
• GPU memory management and infrastructure tuning.
• Expert with GitLab CI/CD and security automation.
• Testing with PyTest and automated test generation.
• Advanced Git workflows and repository mirroring.

Responsibilities

• Architect, deploy, and scale open-source models using distributed frameworks (Kubernetes, Ray, Slurm).
• Design ML systems and data pipelines; run experiments and benchmark performance.
• Build analytics dashboards to communicate model performance to stakeholders.
• Own features from ideation to production across code repositories and teams.

Skills

Advanced degree in CS/EE

Python & Systems Eng

LangChain

HuggingFace

TensorFlow

PyTorch

Scikit-learn

Data analysis (Python)

GPU memory management

GitLab CI/CD

PyTest

Git workflows

Education

Master’s or PhD in Computer Science / Electrical Engineering

Tools

Kubernetes

Ray

Slurm

Job description

NVIDIA is seeking a talented Machine Learning Engineer to drive end-to-end lifecycle management of AI-powered systems across distributed infrastructure. You will deploy and scale models, manage GPU orchestration, and build automated testing and CI/CD pipelines using GitLab.

The role demands strong Python engineering skills, experience with Kubernetes, Ray, Slurm, and ML frameworks, and a track record of delivering production-grade AI workflows at scale in a fast-paced environment.