Distinguished Technologist, Edge AI Architect
$174,050–$278,450 year
On-sitePalo Alto, California, United States or Spring, Texas, United States
Job Summary
Architect the full-stack Edge AI platform from silicon enablement through model lifecycle governance, defining cross-layer interfaces for hardware, OS, inference serving, and agentic runtimes. Own the multi-year technical roadmap, establishing engineering standards for LLM orchestration, secure boot, and cost-optimized edge-to-cloud handoffs. Drive the control plane, telemetry, and fleet management frameworks that ensure scalable, observable deployments across heterogeneous silicon. Partner with firmware, security, and business teams to resolve cross-layer trade-offs, shape the platform portfolio, and translate strategic direction into business impact for executive leadership and customers.
Required Qualifications
- Four-year or Graduate Degree in Computer Science, Information Technology, Software Engineering, or any other related discipline or commensurate work experience or demonstrated competence
- Typically has 12+ years of work experience, preferably in software designing & development, software architecture, programming languages, or a related field
- Demonstrated experience architecting across multiple layers of a modern AI stack — from hardware/OS enablement and inference serving to agentic runtimes and model lifecycle
Desired Qualifications
- Programming Language Certification (Python, C++, Rust, Java, or similar)
- Cloud or platform architecture certification (AWS, Azure, or CNCF/Kubernetes)
- LLM, vLM, and multi-modal model architecture and orchestration
- Agentic AI systems and runtimes (agent harnesses, tool use, sandboxing, governance)
- Inference serving and optimization (model serving, inference gateways, model/request routing)
- Edge AI and edge-to-cloud architecture (latency, cost, privacy, on-device constraints)
- Model management, registry, lifecycle, versioning, and provenance
- GPU/accelerator computing and heterogeneous silicon (CUDA and related)
- Hardware/software co-design and silicon abstraction layers
- Security foundations: chain of trust, secure boot, isolation, sandboxing, and confidential computing
- Fleet management, control planes, observability, and telemetry
- Distributed systems and scalability
- Kubernetes, Docker, and containerized/microservices architecture
- Python, C++, Rust (systems-level and ML tooling)
- MLOps / LLMOps and CI/CD for models and agents
- Cloud platforms (AWS, Microsoft Azure) and hybrid deployment
- Cost, latency, and performance optimization at scale
- DevOps and automation
- Software engineering and full-stack development
- APIs and interface/contract design across layers
- Effective Communication
- Results Orientation
- Learning Agility
- Digital Fluency
- Customer Centricity
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