Used Tools & Technologies
Not specified
Required Skills & Competences ?
Docker @ 4 Grafana @ 3 Kubernetes @ 4 Prometheus @ 3 Python @ 4 Spark @ 4 CI/CD @ 4 Distributed Systems @ 4 Machine Learning @ 3 TensorFlow @ 3 Communication @ 4 JavaScript @ 6 MongoDB @ 4 React @ 6 Node.js @ 6 Microservices @ 4 Debugging @ 7 API @ 4 Hadoop @ 4 PyTorch @ 3 GPU @ 4Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, we're at the forefront of AI innovation powering breakthroughs in research, autonomous vehicles, robotics, and more. The DGX Cloud team builds and operates the AI infrastructure that fuels this progress.
Responsibilities
- Design, develop, and deploy full-stack web applications to support large-scale AI infrastructure operations and workflows
- Collaborate with AI and ML research teams to identify pain points and deliver tools that accelerate their work
- Develop APIs, backend services, and UIs to improve visibility, observability, and control over large-scale GPU clusters
- Develop backend services to manage job schedulers and cluster operations
- Define and track metrics that measure efficiency, resiliency, and developer productivity across the platform
- Drive engineering excellence in testing, CI/CD, code quality, and performance
- Lead architectural discussions and mentor junior engineers on design and implementation
- Stay ahead of AI/ML infrastructure trends and drive adoption of best practices within the team
Requirements
- 8+ years of experience in developing software infrastructure for large scale AI systems
- Bachelor's degree or higher in Computer Science or a related technical field (or equivalent experience)
- Proficiency with full-stack development: JavaScript (Vue or React), Node.js, Python, and/or Golang, scripting languages
- Experience with distributed systems and cloud-native technologies (Docker, Kubernetes, microservices)
- Familiarity with observability stacks: ELK, OpenSearch, Prometheus, Grafana, or Loki
- Strong debugging and root cause analysis skills across application and infrastructure layers
- Experience with large-scale AI training, inference, or data infrastructure services
- Excellent communication, collaboration, problem solving, and a growth mindset
Ways to Stand Out From the Crowd
- Experience building developer platforms or self-service internal infrastructure tools for efficiency, resiliency, or observability
- Hands-on experience as a Machine Learning Engineer (MLE) or deep familiarity with DL frameworks (e.g., PyTorch, TensorFlow, JAX, Ray)
- Hands-on experience operating at datacenter scale, including GPU cluster debugging and root cause analysis
- Experience with MongoDB, Hadoop, or Spark
At NVIDIA, you'll be immersed in a diverse, supportive environment where you're empowered to do your best work. The DGX Cloud AI Infrastructure team is at the core of NVIDIA's AI efforts building the software that makes scalable research possible. Join us and help power the next wave of innovation.