Head of AI

Role: Senior Manager, AI Engineering & Product
Location: San Jose, CA (Hybrid)
Duration: 3+ months


Overview:

• Senior hybrid IC and people leadership role bridging AI systems architecture, agentic AI, and cross-functional product execution
• Operating level: IC10 / L7 or equivalent (Client, Google, Amazon, or high-growth AI startup calibre)
• Expected to own roadmap, stakeholder alignment, and end-to-end delivery independently

Must Have: Applied AI, LLM Systems & Inference Optimization, AI Agents & Agentic Frameworks, Distributed AI Infrastructure, AI Platform Architecture, Engineering & People Leadership

Key Responsibilities:

• Build and scale AI deployment platforms focused on inference speed, latency reduction, and model acceleration
• Architect Client software libraries and tooling to push LLM inference and training optimization
• Design and lead multi-agent engineering systems including orchestration, parallelism, and tool usage
• Prototype and incubate R&D innovations with potential patent value
• Drive cross-functional alignment and secure R&D budget from senior leadership
• Lead engineering teams with full autonomy across roadmap, staffing, and delivery

Required Qualifications:

• 15+ years in engineering, with 8 to 9 years in applied AI
• Group Manager or Director level experience at a large tech company or high-growth AI startup
• Deep hands-on expertise in LLM systems: transformers, inference optimization, quantization, KV cache, distributed training (PyTorch FSDP, PEFT/LoRA)
• Production-grade experience with AI Agents and agentic frameworks (Claude Code, Agent SDK, or equivalent)
• Infrastructure at scale: Kubernetes, Kafka, Spark, multi-tenant SaaS, AWS, Google Cloud Platform
• Proven record of building AI platforms from zero to enterprise production (F500 clients preferred)
• Experience with vector databases, synthetic data pipelines, RAG, Chain of Thought

Nice to Have:

• Published author or recognized thought leader in AI/ML
• Startup founding or enterprise incubation experience
• GPU hardware ecosystem familiarity: CXL, NVMe, PCIe-level AI optimization
• Stanford GSB or equivalent advanced education