🛠 Tech Stack

💼 About This Role

You'll build and lead a team developing performance modeling frameworks to guide AI infrastructure designs. Your work will directly influence reference architectures and vendor roadmaps through quantitative analysis. You'll partner with research, software, and hardware teams to optimize system-level tradeoffs for advanced AI workloads.

🎯 What You'll Do

• Build and own performance modeling framework/toolchain for AI systems.
• Analyze architectural tradeoffs across compute, memory, networking.
• Translate modeling outputs into clear recommendations for teams and vendors.
• Lead and grow a small team of 2–3 engineers.

📋 Requirements

• Experience owning or building performance modeling frameworks for system design decisions.
• Deep knowledge of AI/ML workloads including training and/or inference at scale.
• Understanding of system-level tradeoffs across compute, memory, networking in large-scale distributed systems.
• Experience using modeling (analytical or simulation) to inform architectural decisions.

✨ Nice to Have

• Experience working with hardware vendors (ODM/JDM, silicon, networking).
• Background in data center infrastructure or hyperscale systems.
• Familiarity with accelerators (GPUs/ASICs) and interconnects (e.g., NVLink, InfiniBand, Ethernet).

🎁 Benefits & Perks

• 💰 Competitive equity package included.
• 🏖️ Hybrid work model with 3 days in office per week.
• 🚚 Relocation assistance provided.
• 🏥 Comprehensive benefits (not specified but implied).
• 🧠 Impactful work on cutting-edge AI infrastructure.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30-45 min
2. 2Technical Interview· 60 min
3. 3Onsite (multiple rounds)· 3-4 hours