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Senior Hardware Product Manager - Edge AI


Product & Project Management

IT & Telecoms GHS Confidential
2 months ago

Job Summary

We are seeking a Senior Hardware Product Manager supporting our Edge AI platforms for robotics, healthcare, industrial and other enterprise applications. You will own product definition, product positioning, product launches, evangelism, sales enablement, market & competitive analysis, and more! You will work closely with many groups within NVIDIA including hardware and software product engineering, marketing, sales, operations and customer support. The successful candidate will be dedicated and passionate about driving new products to market.

  • Minimum Qualification: Diploma
  • Experience Level: Senior level
  • Experience Length: 8 years

Job Description/Requirements

What you will be doing:

  • Lead the development of IGX, compute platform for enterprise edge AI.
  • Understand customer use cases and translate them to product requirements.
  • Propose innovative solutions to sophisticated technical problems.
  • Build compelling Value Proposition, educate sales and marketing.
  • Develop demos to highlight outstanding product capabilities at product launch and other events
  • Partner with cross-functional teams including engineering, operations, marketing and sales to own the go-to-market activities, inclusive of performing market and competitive analysis.
  • Define and supervise benchmarks and critical metrics for highlighting the features of the product
  • Liaison between the sales and engineering teams to provide pre and post sales customer support, and work with the executive team to understand priorities and present tradeoffs

What we like to see:

  • BS/MS/PhD in Computer Science or Engineering or equivalent degree
  • 8+ years of overall experience
  • Experience in teamwork across multi-functional teams
  • Excellent verbal and written communication skills.
  • Knowledge of system on chip designs for embedded markets.
  • Good understanding computer architecture.
  • Familiarity with embedded system software and board design process.
  • Knowledge of Deep Learning architectures and GPUs.

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