Principal Engineer, AI BU
$196,350–$292,600 year
On-siteSan Jose, California, United States
Job Summary
Architect and lead complex, cross-team initiatives at the intersection of storage, data, and AI, owning system design end-to-end. Set technical direction and design standards across multiple engineering teams, authoring design docs and RFCs to resolve ambiguous, cross-cutting problems. Write production code, review highest-stakes designs, and personally unblock difficult engineering challenges while mentoring engineers to raise the technical bar. Partner with leadership to translate business priorities into technical roadmaps, represent the AI BU in executive reviews, and champion best practices in observability and operational excellence. Identify and drive resolution of systemic technical risks including scalability bottlenecks and architectural debt.
Required Qualifications
- 12+ years of hands-on software engineering experience, including designing and building large-scale distributed systems
- Demonstrated track record as technical lead or architect on multi-team or org-wide initiatives
- Deep hands-on experience with cloud-native architectures on AWS, Azure, or Google Cloud
- Well versed in using agentic engineering using local and/or frontier models
- Hands-on experience with Distributed Storage Systems
- Proven ability to build technical consensus and influence engineering direction across teams without formal authority
- Strong system design skills, with direct ownership of architecture for highly available, low-latency, large-scale services
- Excellent written and verbal communication skills — able to author design docs/RFCs and present technical trade-offs to engineers and executives alike
- Experience with data processing, analytics, and AI/ML systems
- Track record of mentoring engineers and raising the technical quality bar across teams
- Prior experience building CI/CD infrastructure for large-scale deployments
Desired Qualifications
- Domain expertise on knowledge graphs, semantics and data modeling
- Hands-on experience with Distributed Database Systems
- Hands-on experience with training and deploying AI models
- Hands-on experience with GPU workloads and accelerated computing infrastructure
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