Senior MLOps Engineer at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

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

Skills

MLOps

ML Pipelines

CI/CD

Observability

GPU Optimization

Dataset Versioning

Experiment Tracking

Model Governance

Large-scale Infrastructure

Production Training

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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