About The Role

Majestic Labs builds power-efficient AI servers for the largest and most advanced AI workloads.

We are looking for a Senior Architect to work on simulation and translating architectural ideas into quantitative, defensible performance models.

This role is ideal for someone who lives at the intersection of:

• Computer architecture
• Workload characterization
• System-level modeling
• AI / data-intensive workloads
  
  

What You’ll Do

• Performance simulator & translate workloads into models
• Extend and maintain system-level performance models (compute, memory, interconnect, scheduling).
• Model system behaviors: NUMA effects, contention, coherence, bandwidth saturation.
• Characterize real workloads (LLMs, GNNs, graph analytics, tabular models and more).
• Guide architectural decisions
• Evaluate architectural trade-offs
• Produce quantitative comparisons vs. existing and emerging AI platforms.
• Work closely with hardware architects, compiler/runtime teams, and external partners.
• Ensure simulator assumptions align with hardware and software reality.
• Support external conversations
• Contribute to technical deep dives with strategic partners and customers.
• Help translate performance modeling into clear narratives for execs and investors.
  
  

Requirements:

Required

What We’re Looking For

• BSc in CS, EE, CE or similar degrees plus 8 years experience in computer architecture, systems research, or performance engineering. Or PhD in CS, EE, CE or similar degrees plus 2 years of relevant experience.
• Strong background in performance modeling or simulation (analytical, event-driven).
• Experience with AI or graph workloads.
• Understanding of: memory hierarchies (NUMA, caches, coherence), Interconnects (PCIe, fabrics, RDMA-), GPU-based systems.
• Hands-on coding experience (Python, C/C++, or similar).
• Ability to reason from first principles, not just benchmarks.
  
  

Strong Plus

• Background in architecture simulators ( gem5-like, in-house tools, or research frameworks).
• Exposure to ML runtimes, compilers, or hardware-aware model design.
• Research background (MS/PhD) in computer systems (CS/EE), architecture, or related fields.