Senior Technical Program Manager, Deep Learning Software

NVIDIA NVIDIA · Semiconductors · Shanghai, China

Senior Technical Program Manager to lead software initiatives and develop Gen AI models enabling NVIDIA’s most advanced AI researchers and engineers. This leader will guide engineering programs using the best industry processes well suited to our fast pace and rapidly expanding roadmap.

What you'd actually do

  1. Engage with cross-company partners to plan programs and coordinate teams to meet key business objectives
  2. Guide engineering programs in all aspects of program management – planning, forecasting, documenting, scheduling, effective meetings, multi-faceted prioritization, management of dependencies, reporting, and effective handling of critical and blocking issues
  3. Guide engineering teams in the use of agile methodologies
  4. Develop and implement metrics for measuring program effectiveness and improvement areas, collect and analyze data in support of planning and data driven decisions
  5. Report on overall program status, providing insights and recommendations to senior management

Skills

Required

  • program management expertise
  • mastery of technical and management practices
  • managing global projects
  • engaging and moderating successful engagements with engineering partners and vendors
  • Exceptional communication and presentation skills
  • strong problem-solving and conflict management skills
  • In-depth understanding of software engineering principles and quality requirements in enterprise systems
  • Strong multitasking abilities
  • Knowledge of agile methodologies and tools
  • project planning
  • task tracking tools
  • Experience in AI training environments
  • resource capacity planning
  • Proactive in identifying and implementing efficient changes in software engineering and release management
  • Excellent organizational skills
  • ability to use project management tools (e.g. Jira, Aha!, Confluence)
  • distributed version control systems (e.g. Git)

Nice to have

  • Background in computer science
  • machine learning
  • deep learning
  • open source software
  • GPU technology
  • production application software development
  • release and support methodology
  • DevOps
  • management of customer workflows using large scale distributed computing
  • working with AI researchers
  • directly training and evaluating AI models
  • driving process improvements
  • measuring efficiency

What the JD emphasized

  • AI training environments
  • resource capacity planning
  • working with AI researchers
  • training and evaluating AI models