Senior MLOps Engineer — AI Infrastructure at Scale

NVIDIA Corporation; Polska; On-site; PLN 293,000 - 650,000; Full time

Job summary

NVIDIA Corporation is seeking a Senior MLOps Engineer to join the DSX Enablement team. You will design and deploy innovative AI infrastructure, advise on ML workloads, and support customers in building scalable AI systems and open-source contributions.

You will work on distributed training, inference optimization, and MLOps pipelines on NeoCloud platforms and NVIDIA Cloud Partners, with focus on reducing latency, cost, and operational risk while enabling end-to-end AI initiatives for internal

Qualifications

• Experience across ML lifecycle from exploration to production
• Strong Linux and datacenter networking knowledge
• Proficient scripting in Python and Bash
• Solid systems programming in C++, Go, or Rust
• Experience with ML frameworks for training and inference

Responsibilities

• Build and optimize MLOps pipelines for DGX Cloud and NCP platforms
• Lead performance tuning for large-scale training and inference workloads
• Collaborate with open-source communities and NVIDIA ecosystem teams
• Diagnose cross-layer issues spanning hardware, network, and software stacks
• Develop reference architectures and tools to simplify ML workloads at scale

Skills

ML engineering

Data science

Data engineering

Linux

Kubernetes

Distributed filesystems

Networking

Python

Bash

C++/Go/Rust

Education

Bachelor's/Master's/PhD in CS or Electrical Eng or related field

Tools

Kubernetes

NGP/Large-scale training frameworks

Docker

Job description

NVIDIA Corporation is seeking a Senior MLOps Engineer to join the DSX Enablement team. You will design and deploy innovative AI infrastructure, advise on ML workloads, and support customers in building scalable AI systems and open-source contributions.

You will work on distributed training, inference optimization, and MLOps pipelines on NeoCloud platforms and NVIDIA Cloud Partners, with focus on reducing latency, cost, and operational risk while enabling end-to-end AI initiatives for internal