Senior MLOps Engineer - DSX Enablement
$184k - $357k • US, CA, Santa Clara • Full-time
Posted 1d ago
Job Location
US, CA, Santa Clara
Also open in Germany, Remote
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their most challenging technical problems. In this role, you will develop innovative solutions that advance AI infrastructure capabilities, advise infrastructure experts on the demands of ML workloads, and help practitioners diagnose and solve full-stack AI and ML system problems. You will work on a team with direct responsibility for the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and supporting new hardware in open-source frameworks.
Responsibilities
- Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners, including distributed training, inference optimization, and MLOps pipelines.
- Act as a primary technical contact for internal and external customers and partners, guiding joint engagements and ensuring the success of initiatives on DGX Cloud.
- Solve complex problems in production environments.
- Work closely with teams building infrastructure software and accelerated frameworks for AI applications.
- Profile and tune large-scale training and inference workloads on NCP platforms to reduce latency, cost, and operational risk.
- Develop open-source tools and reference architectures for building and managing machine learning and AI workloads at scale.
Requirements
- BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or equivalent experience.
- 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, targeting large-scale production systems.
- Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle.
- Proficiency with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking.
- Solid scripting and programming skills in bash and Python.
- Solid systems programming skills in C++, Go, or Rust.
- Experience using machine learning or deep learning frameworks for training and inference.
- Excellent communication and technical presentation skills.
- A clear record of engineering discipline and execution on projects.
Benefits
- Competitive salaries
- Generous benefits package
- Equity
- Eligibility for equity and benefits