Manager, Deep Learning Algorithms

NVIDIA · Semiconductors · Santa Clara, CA

Manager for Deep Learning Algorithms at NVIDIA, focusing on productizing DL models, optimizing inference, and leading engineering teams. The role involves working with LLMs/VLMs, inference optimization, and collaborating across NVIDIA to develop state-of-the-art algorithms for GPU-accelerated platforms.

What you'd actually do

  1. Plan, schedule, mentor, and lead the execution of projects and activities of the team. Including creating, optimizing, and deploying inference DL workloads.
  2. Collaborate with internal customers to align priorities across business units
  3. Coordinate projects across different geographic locations
  4. Grow and develop a world-class team
  5. About 10% travel is required for this job. You will be traveling to conferences, other sites, or visit customers occasionally

Skills

Required

  • Deep learning
  • Algorithmic background
  • Large scale LLM/VLM deployment
  • Inference optimization
  • Leadership experience
  • Project execution
  • Cross-functional collaboration
  • Team leadership and development
  • LLMs
  • VLMs
  • Programming
  • Debugging
  • Performance analysis
  • Test design

Nice to have

  • Inference of DL models
  • Performance analysis and tuning
  • Inference platforms such as TensorRT-LLM, vLLM, and SGLang
  • Project management tools (e.g. JIRA, Microsoft Project)

What the JD emphasized

  • Ability to work in a multifaceted, product-centric environment is required
  • excellent interpersonal skills are also a requirement
  • Minimum requirement of BSc or equivalent experience
  • 8+ overall years related of overall experience, including 3 years of management/leadership experience
  • Experience leading multiple software engineering projects
  • Strong experience with Large Language Models (LLMs) and Large Visual-Language Models (VLMs)
  • Excellent programming, debugging, performance analysis, and test design skills
  • Great communication

Other signals

  • leading engineering activities related to productizing Deep Learning models
  • implement and improve the latest algorithms
  • large scale LLM/VLM deployment, inference optimization
  • highly optimized novel and state-of-the-art numerical, analytics, and deep learning algorithms
  • creating, optimizing, and deploying inference DL workloads