ML Engineer: Scalable AI Pipelines & GPU Orchestration

Thomas To; Santa Clara (CA); On-site; USD 170,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

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

You will work on Python-based systems, implement secure CI/CD pipelines with GitLab, and perform rigorous model benchmarking, testing, and optimization for high-throughput AI workloads.

Qualifications

• Master's degree or PhD in CS/EE or equivalent experience.
• 3+ years of professional Python production-grade asynchronous development.
• Deep experience with LangChain, Hugging Face, vLLM, and SGLang; plus TensorFlow, PyTorch, Scikit-learn.
• Data analysis with Python (pandas, NumPy) and communicating results clearly to colleagues.
• Experience deploying, monitoring, and scaling models with Kubernetes, Ray, or Slurm.
• Strong GPU memory management and infra-level tuning for AI inference workloads.
• Advanced GitLab CI/CD pipelines and MR security automation.
• Testing with PyTest, mocks, and AI workload test generation.
• Advanced Git workflows (rebases, signing, mirroring).

Responsibilities

• Architect, deploy, and scale open-source models using Kubernetes, Ray, or Slurm.
• Design ML systems and data pipelines; prompt-tune and deploy production models.
• Run model benchmarks, perform error and gap analysis, and build dashboards.
• Own features from ideation to production; coordinate updates across repos and teams.

Skills

Python programming

Asynchronous programming

Machine learning systems design

Distributed computing

Data analysis

Code testing

Education

Master's degree or PhD in CS/EE

Tools

Kubernetes

Ray

Slurm

LangChain

Hugging Face

vLLM

SGLang

TensorFlow

PyTorch

Scikit-learn

GitLab CI/CD tooling

Job description

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

You will work on Python-based systems, implement secure CI/CD pipelines with GitLab, and perform rigorous model benchmarking, testing, and optimization for high-throughput AI workloads.