Job Description

NVIDIA is looking for a Senior MLOps Engineer to design and scale infrastructure for AI research and product development. The role involves collaborating with research scientists and product teams to build robust ML pipelines and scalable systems, ensuring efficient training and reproducible deployments.

Requirements

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

Responsibilities

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

Benefits

Employees at NVIDIA are often offered comprehensive, day-one benefits—including medical, dental, and vision coverage with HSA support, life and disability insurance, an Employee Assistance Program, and a 401(k) with auto-enrollment. Many roles also have generous time off and holidays, donation matching (up to $10,000), and a wide menu of extras like FSAs, commuter benefits, legal and identity-theft protection, pet insurance, and wellness discounts. Optional programs can include student-loan and home-purchase support, plus family care resources and expert medical services.