Field Application Engineer Manager, Cloud AI Infrastructure

Apigee
Apigee

Software Engineering, Other Engineering, Data Science

Austin, TX, USA · Kirkland, WA, USA

Posted on Sep 25, 2026
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In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
  • Health, dental, vision, life, disability insurance
  • Retirement Benefits: 401(k) with company match
  • Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
  • Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
  • Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
  • Baby Bonding Leave: 18 weeks
  • Holidays: 13 paid days per year
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Austin, TX, USA; Kirkland, WA, USA.

Minimum qualifications:

  • Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or IT-related field, or equivalent practical experience.
  • 8 years of experience with Linux/Unix systems and experience in debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
  • 5 years of experience in technical leadership.
  • 3 years of experience with technical infrastructure (e.g., deployment, maintenance, and troubleshooting), and with quality and reliability of technical infrastructure.
  • 3 years of debug or validation experience with CPU, dGPU, or TPU.
  • Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance).

Preferred qualifications:

  • Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
  • Experience with systems automation, and with systems design and debug.
  • Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices.
  • Experience with ML frameworks (e.g., TensorFlow, PyTorch), and understanding of the AI/ML training and inference lifecycle.
  • Advanced understanding of memory and high-speed IO technologies.
  • Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
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