Amazon
Principal and Senior Principal Engineer, AWS Compute and ML Services
$200K–$311K
Seattle, WA +5 · Principal · Full-time · Posted Sep 30, 2026
- Location
- Seattle, WA
Arlington, VA
Santa Clara, CA
New York, NY
Herndon, VA
Cupertino, CA - Experience
- 10+ years required; preferred qualification states 15–25+ years of deep hands-on technical expertise.
About the job
Leads the architecture and delivery of AWS compute and machine-learning infrastructure, setting technical direction for large-scale systems across hardware, software, security, and customer-facing services.
What you'll do
- Own architecture and hands-on delivery of critical systems, making end-to-end decisions across hardware interfaces and customer APIs.
- Lead cross-organization initiatives, shape technical vision and roadmaps, and mentor senior engineers.
- At Senior Principal level, set multi-year direction and drive architectural transformations across AWS Compute and ML Services.
What they ask for
- 10+ years of non-internship professional software development experience.
- Experience owning technical architecture and end-to-end delivery, and partnering with senior leadership.
- Track record of leading multiple concurrent technical initiatives and driving lasting decisions.
- Experience influencing across organizational boundaries without positional authority.
Nice to have
- 15–25+ years of deep hands-on technical expertise in complex distributed systems.
- Experience with large-scale systems programming, virtualization, operating systems, performance engineering, or hardware-software co-design.
Pay
230,100.00 - 311,200.00 USD annually (base salary), Cupertino; 230,100.00 - 311,200.00 USD annually (base salary), Santa Clara; 220,100.00 - 297,700.00 USD annually (base salary), New York; 200,100.00 - 270,600.00 USD annually (base salary), Arlington; 200,100.00 - 270,600.00 USD annually (base salary), Herndon; 200,100.00 - 270,600.00 USD annually (base salary), Seattle
Tools
EC2 (Elastic Compute Cloud), SageMaker, Bedrock, PCI
AI focus
Architects and leads AI/ML infrastructure, including platforms for generative AI at scale, AI model marketplaces, and infrastructure for large training and inference workloads.
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