Our technology has no boundaries! NVIDIA is creating the world’s most groundbreaking and innovative compute platforms for the world to use! It’s because of our work that data engineers and data scientists can advance their ideas. We are looking for individuals to develop a data processing and ML platform for data scientists to use.
As a data processing platform engineer, you will design, implement and operate Kubernetes based GPU accelerated data processing service at scale, with high availability and reliability. You will lead and encourage adoption of the data processing service, your work should improve time to first query (TTFQ) metrics, drive platform engagement metrics, and come up with innovative solutions that blends with pioneering Nvidia's enterprise scale data science platform.
What you’ll be doing:
Optimize distributed computing infrastructure by analyzing cost and right sizing for latency and performance
Identify KPIs and instrument metrics tracking for dashboards and alerting
Enhancing and maintaining a robust scale, reliable, real-time data processing service
Train data engineers, data scientists and production engineers how to adopt data processing workflows
Participate in on-call rotation, run-book implementation and continuous process improvement
What we need to see:
Experience architecting, developing and deploying large-scale distributed systems at scale
Strong Kubernetes experience on-premise and/or CSP, developing containerized microservices
Hands on experience in the performance tuning/troubleshooting Spark applications
Experience with distributed systems, databases, and big data systems like Ray, Spark Rapids
Familiarity with metrics collection, health monitoring, and observability tools
Building, operating and maintaining full stack software deployments coupled with excellent software programming skills
Master's or Bachelor's degree in Computer Science or Electrical Engineering or CE or equivalent experience.
A minimum of 5yrs experience with a background in software engineering and math
Ways to stand out from the crowd:
Prior data processing at scale on Nvidia GPUs
Experience with CUDA and/or using Nvidia GPUs for ML/DL
You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.
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NVIDIA is a publicly traded, multinational technology company headquartered in Santa Clara, California. NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, and ignited the era of modern AI.
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