Staff Field Application Engineer, Customer Success

Tenstorrent · Semiconductors · Santa Clara, CA · Experience

Field Application Engineer role focused on customer success and driving adoption of Tenstorrent's AI products and solutions. Requires strong technical knowledge in AI/ML, customer-facing skills, and experience with AI technologies and frameworks, embedded systems, and AI accelerators. The role involves collaborating with sales and product teams, understanding customer challenges, and providing solutions. Experience with hardware/software co-optimization for edge inference is important.

What you'd actually do

  1. Collaborate closely with the sales team and enterprise customers, leveraging your deep technical knowledge in AI to drive the adoption of our products and solutions.
  2. Work directly with customers in meetings, presentations, and workshops to understand their technical challenges and provide effective solutions.
  3. Partner closely with sales operations and product teams to provide feedback from customers and help shape future product enhancements.
  4. Your field insight will help shape our roadmap, our features, and the way we show up for developers.
  5. Experience with embedded systems, FPGA Architecture or AI accelerators. Familiarity with hardware/software co-optimization is often critical for efficient inference at the edge.

Skills

Required

  • 5+ years of relevant technical, enterprise experience (such as Sales Engineer, Solutions Engineer, or Technical Account Manager)
  • Proven expertise in AI technologies and frameworks, including machine learning, deep learning, natural language processing (NLP),computer vision, PyTorch or TensorFlow.
  • Experience with embedded systems, FPGA Architecture or AI accelerators.
  • Good understanding of data center, server architecture and deployment, thermal/power, and infrastructure management.
  • Strong customer-facing skills
  • Excellent communication skills

Nice to have

  • Familiarity with hardware/software co-optimization is often critical for efficient inference at the edge.

What the JD emphasized

  • customer-facing
  • technical expertise
  • AI technologies and frameworks
  • inference at the edge
  • hardware/software co-optimization