Principal Machine Learning Engineer

Oracle Oracle · Enterprise · Seattle, WA +1

Principal Machine Learning Engineer on the Generative AI Services team at Oracle, responsible for leading the architecture, design, and development of scalable systems for AI model training, fine-tuning, and inference. The role involves optimizing AI infrastructure, analyzing model architectures, troubleshooting issues across the AI model lifecycle, and contributing to open-source frameworks like vLLM and SGLang. This is a leadership role guiding a team to deliver on the roadmap.

What you'd actually do

  1. lead the architecture, design and development of distributed, scalable, and high-performance systems for AI model training, fine-tuning, and inference
  2. build and optimize next-generation AI infrastructure that powers large-scale generative AI workloads
  3. lead analyzing model architectures to improve performance, efficiency, and scalability, while leveraging cutting-edge technologies to develop state-of-the-art AI systems and onboard frontier models
  4. benchmark, diagnose, troubleshoot, and resolve issues across the AI model lifecycle, including training, fine-tuning, and serving, ensuring reliable and efficient production deployments
  5. contribute to open source frameworks such as vLLM, and SGLang and strengthen the position of OCI

Skills

Required

  • architecture, design and development of distributed, scalable, and high-performance systems for AI model training, fine-tuning, and inference
  • AI model lifecycle management (training, fine-tuning, serving)
  • performance, efficiency, and scalability analysis of model architectures
  • troubleshooting and issue resolution in AI production deployments
  • contribution to open source frameworks (e.g., vLLM, SGLang)
  • leadership and team guidance

Nice to have

  • generative AI
  • large-scale generative AI workloads
  • frontier models

What the JD emphasized

  • lead the architecture, design and development of distributed, scalable, and high-performance systems for AI model training, fine-tuning, and inference
  • build and optimize next-generation AI infrastructure that powers large-scale generative AI workloads
  • lead analyzing model architectures to improve performance, efficiency, and scalability, while leveraging cutting-edge technologies to develop state-of-the-art AI systems and onboard frontier models
  • benchmark, diagnose, troubleshoot, and resolve issues across the AI model lifecycle, including training, fine-tuning, and serving, ensuring reliable and efficient production deployments
  • contribute to open source frameworks such as vLLM, and SGLang and strengthen the position of OCI
  • lead a team of senior and junior members and guide them to deliver the roadmap on time with high quality

Other signals

  • leading architecture, design, and development of distributed, scalable, and high-performance systems for AI model training, fine-tuning, and inference
  • build and optimize next-generation AI infrastructure that powers large-scale generative AI workloads
  • contribute to open source frameworks such as vLLM, and SGLang