Senior ML Platform 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

Artificial Intelligence, High-Performance Computing, Machine LearningIndustries

Requirements

BS/MS in Computer Science, Engineering, or equivalent experience
7+ years in software/platform engineering, including 3+ years in ML infrastructure or distributed compute systems
Solid understanding of ML training/inference workflows and lifecycle—from data preprocessing to deployment
Proficiency in crafting and operating containerized workloads with Kubernetes, Docker, and workload schedulers
Experience with ML orchestration tools such as Kubeflow, Flyte, Airflow, or Ray
Strong coding skills in languages such as Python, Go, or Rust
Experience running Slurm or custom scheduling frameworks in production ML environments
Familiarity with GPU computing, Linux systems internals, and performance tuning at scale

Responsibilities

Design, build, and maintain scalable ML platforms and infrastructure for training and inference on large-scale, distributed GPU clusters
Develop internal tools and automation for ML workflow orchestration, resource scheduling, data access, and reproducibility
Collaborate with ML researchers and applied scientists to optimize performance and streamline end-to-end experimentation
Evolve and operate multi-cloud and hybrid (on-prem + cloud) environments with a focus on high availability and performance for AI workloads
Define and monitor ML-specific infrastructure metrics, such as model efficiency, resource utilization, job success rates, and pipeline latency
Build tooling to support experimentation tracking, reproducibility, model versioning, and artifact management
Participate in on-call support for platform services and infrastructure running critical ML jobs
Drive the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (e.g., GB200, NVLink, etc.)

Skills

Key technologies and capabilities for this role

GPUDistributed SystemsML PlatformsWorkflow OrchestrationResource SchedulingMulti-CloudHybrid CloudInfrastructure MonitoringModel VersioningNVLinkGB200

Questions & Answers

Common questions about this position

What experience level is required for this Senior ML Platform Engineer role?

A BS/MS in Computer Science, Engineering, or equivalent experience is required, along with 7+ years in software/platform engineering, including 3+ years in ML infrastructure or distributed compute systems.

What key technical skills are needed for this position?

Proficiency in Kubernetes, Docker, and workload schedulers is required, along with experience in ML orchestration tools such as Kubeflow, Flyte, Airflow, or Ray, strong coding skills in Python, Go, or Rust, and familiarity with GPU computing and Linux systems internals.

Is this a remote position, or does it require working in an office?

This information is not specified in the job description.

What is the compensation or salary for this role?

This information is not specified in the job description.

What makes a candidate stand out for this ML Platform Engineer position?

Candidates stand out with experience building or operating ML platforms supporting PyTorch, TensorFlow, or JAX at scale, deep understanding of distributed training techniques like data/model parallelism, Horovod, NCCL, expertise with infrastructure-as-code tools such as Terraform and Ansible, and passion for building developer-centric platforms.

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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