Senior MLOps Engineer: Scale AI Pipelines & Inference

NVIDIA AI; Seattle (WA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Senior MLOps Engineer to join the DSX Enablement team in Seattle, collaborating with customers to implement and optimize AI workloads across production environments.

You will help design and run large-scale training and inference pipelines, advise on infrastructure needs, and contribute to open-source tooling while working with cutting-edge hardware and software stacks. Equity and comprehensive benefits accompany the role.

Qualifications

• BS/MS/PhD in CS, CE, or related field, or equivalent experience.
• 8+ years in data science, data engineering, or ML engineering for large-scale systems.
• AI/ML lifecycle experience from exploration to production.
• Linux, batch schedulers, Kubernetes, distributed filesystems, and datacenter networking.
• Bash, Python; systems programming in C++, Go, or Rust.
• Experience with ML/DL frameworks for training and inference.
• Excellent communication and presentation skills.

Responsibilities

• Build and deploy AI solutions on NeoCloud and NVIDIA Cloud Partners, including distributed training and MLOps pipelines.
• Act as primary technical contact for internal/external customers and partners; guide engagements; solve advanced production problems.
• Collaborate with infrastructure and accelerated-framework teams to support AI applications.
• Profile and tune large-scale training/inference workloads to reduce latency, cost, and risk.
• Develop open-source tools and reference architectures to simplify ML workloads at scale.

Skills

MLOps

Distributed training

Python

C++

Go

Rust

Kubernetes

CI/CD

Performance tuning

Open-source

Education

BS/MS/PhD in CS/EE or related field

Equivalent experience

Tools

DGX

CUDA

NeMo

RAPIDS

Triton

NIM

NVLink

InfiniBand

Kubernetes

Docker

Job description

NVIDIA is seeking a Senior MLOps Engineer to join the DSX Enablement team in Seattle, collaborating with customers to implement and optimize AI workloads across production environments.

You will help design and run large-scale training and inference pipelines, advise on infrastructure needs, and contribute to open-source tooling while working with cutting-edge hardware and software stacks. Equity and comprehensive benefits accompany the role.