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Field Application Engineer, Cloud AI Infrastructure
Job in
Kirkland, King County, Washington, 98034, USA
Listed on 2026-10-05
Listing for:
Apigee
Full Time
position Listed on 2026-10-05
Job specializations:
-
IT/Tech
Systems Engineer, Unix/Linux
Job Description & How to Apply Below
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
- Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
- 2 years of debug or validation experience with CPU, dGPU, or TPU.
- 2 years of experience with technical infrastructure (deployment or maintenance, and troubleshooting), and with quality and reliability of technical infrastructure.
- 2 years of experience with hardware debug (e.g., silicon, platform, IO interface, or memory analysis).
- Experience with Linux/Unix systems and debugging issues across hardware/software boundary on enterprise-grade server infrastructure.
- Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance).
- 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 vendors or customers.
- Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices.
- Experience with ML frameworks (e.g., Tensor Flow, PyTorch), and understanding of the AI/ML training and inference lifecycle.
- Advanced understanding of memory and high-speed IO technologies.
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