NVIDIA Logo

NVIDIA

Senior MLOps Engineer

Posted 19 Days Ago
Be an Early Applicant
In-Office or Remote
2 Locations
184K-357K
Senior level
In-Office or Remote
2 Locations
184K-357K
Senior level
As a Senior MLOps Engineer at NVIDIA, you'll design infrastructure for AI research, build ML pipelines, and collaborate with teams to optimize ML workflows and deployment scalability.
The summary above was generated by AI

NVIDIA is seeking a Senior MLOps Engineer to help design and scale the infrastructure that powers our AI research and product development. In this role, you will partner closely with research scientists and product teams to accelerate their success on the latest GPU and accelerator platforms. By building robust ML pipelines and scalable systems, you will ensure that cutting-edge hardware innovations translate directly into faster experiments, more efficient training, and reproducible deployments at scale. This is an opportunity to shape how NVIDIA’s world-class research teams turn ideas into breakthroughs.

What you’ll be doing:

  • Identify infrastructure and software bottlenecks to improve ML job startup time, data load/write time, resiliency, and failure recovery.

  • Translate research workflows into automated, scalable, and reproducible systems that accelerate experimentation.

  • Build CI/CD workflows tailored for ML to support data preparation, model training, validation, deployment, and monitoring.

  • Develop observability frameworks to monitor performance, utilization, and health of large-scale training clusters.

  • Collaborate with hardware and platform teams to optimize models for emerging GPU architectures, interconnects, and storage technologies.

  • Develop guidelines for dataset versioning, experiment tracking, and model governance to ensure reliability and compliance.

  • Mentor and guide engineering and research partners on MLOps patterns, scaling NVIDIA’s impact from research to production.

  • Collaborate with NVIDIA Research teams and the DGX Cloud Customer Success team to enhance MLOps automation continuously.

What we need to see:

  • BS in Computer Science, Information Systems, Computer Engineering or equivalent experience

  • 8+ years of experience in large-scale software or infrastructure systems, with 5+ years dedicated to ML platforms or MLOps.

  • Proven track record designing and operating ML infrastructure for production training workloads.

  • Expert knowledge of distributed training frameworks (PyTorch, TensorFlow, JAX) and orchestration systems (Kubernetes, Slurm, Kubeflow, Airflow, MLflow).

  • Strong programming experience in Python plus at least one systems language (Go, C++, Rust).

  • Deep understanding of GPU scheduling, container orchestration, and cloud-native environments.

  • Experience integrating observability stacks (Prometheus, Grafana, ELK) with ML workloads.

  • Familiarity with storage and data platforms that support large-scale training (object stores, feature stores, versioned datasets).

  • Strong communication abilities, collaborating effectively with research teams to transform requirements into scalable engineering solutions.

Ways to stand out from the crowd:

  • Practical experience supporting research teams in expanding models on the newest GPU or accelerator hardware.

  • Contributions to open-source MLOps or ML infrastructure projects.

  • Proficiency in optimizing multi-node training tasks throughout extensive GPU clusters and familiarity with extensive ETL and data pipeline software/infrastructure for both structured and unstructured data.

  • Knowledge of security, compliance, and governance requirements for ML in regulated environments.

  • Demonstrated capability in connecting research and production by directing scientists on guidelines while providing reliable infrastructure.

NVIDIA is at the forefront of pioneering advancements in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our innovation, acts as the visual cortex of modern computers and forms the core of our offerings. Our efforts unlock new realms for exploration, facilitate outstanding creativity and discovery, and drive what were previously science-fiction innovations—from artificial intelligence to autonomous vehicles. We are seeking remarkable individuals like you to assist us in propelling the next AI wave!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 6, 2025.NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Top Skills

Airflow
C++
Elk
Go
Grafana
Jax
Kubeflow
Kubernetes
Mlflow
Prometheus
Python
PyTorch
Rust
Slurm
TensorFlow

Similar Jobs

15 Hours Ago
In-Office or Remote
Seattle, WA, USA
144K-162K Annually
Senior level
144K-162K Annually
Senior level
Cloud • Professional Services • Analytics • Business Intelligence • Consulting
The Senior MLOps Engineer will support data science teams by creating frameworks, developing ML models, collaborating on projects, and ensuring model deployment and observability.
Top Skills: Aws SagemakerDockerGitHadoopJenkinsKubernetesPythonPyTorchRedshiftSparkTensorFlowTerraform
6 Days Ago
In-Office or Remote
6 Locations
192K-305K
Senior level
192K-305K
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
NVIDIA is seeking a Senior AI MLOps Engineer to develop training for partners on AI and MLOps, focusing on effective deployment and support of NVIDIA AI infrastructure.
Top Skills: Cloud EnvironmentsCudaDockerKubernetesLinuxNvidia Software StackPyTorchRapidsTensorFlow
13 Days Ago
Remote
USA
Senior level
Senior level
Fitness
Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.
Top Skills: AWSCloudwatchDynamoDBEcsFlinkKafkaKinesisKubeflowLambdaMlflowPythonPyTorchSagemakerTensorFlowVertex Ai

What you need to know about the Charlotte Tech Scene

Ranked among the hottest tech cities in 2024 by CompTIA, Charlotte is quickly cementing its place as a major U.S. tech hub. Home to more than 90,000 tech workers, the city’s ecosystem is primed for continued growth, fueled by billions in annual funding from heavyweights like Microsoft and RevTech Labs, which has created thousands of fintech jobs and made the city a go-to for tech pros looking for their next big opportunity.

Key Facts About Charlotte Tech

  • Number of Tech Workers: 90,859; 6.5% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Lowe’s, Bank of America, TIAA, Microsoft, Honeywell
  • Key Industries: Fintech, artificial intelligence, cybersecurity, cloud computing, e-commerce
  • Funding Landscape: $3.1 billion in venture capital funding in 2024 (CED)
  • Notable Investors: Microsoft, Google, Falfurrias Management Partners, RevTech Labs Foundation
  • Research Centers and Universities: University of North Carolina at Charlotte, Northeastern University, North Carolina Research Campus

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account