Nvidia Senior MLOps Engineer 

United States, Texas 

493268606

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 pipelinesoftware/infrastructurefor 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.