Etched
Performance Modeling Engineer
About this role
Etched seeks a Performance Modeling Engineer to develop analytical models and analyze deep learning workloads on custom inference hardware. You'll identify architectural bottlenecks, drive hardware-software co-optimization, and inform next-generation chip design decisions for an AI infrastructure startup backed by top-tier investors.
What you'll do
- Build performance models and projections across varying workloads and system configurations
- Profile deep learning workloads on hardware to identify micro-architectural bottlenecks
- Drive hardware/software co-optimization by analyzing architectural features for performance gains
- Validate performance models against real systems and silicon through regression testing
- Pathfind architectural decisions during design and proof-of-concept phases
- Analyze inference serving workloads and system-level performance implications
What they're looking for
- Performance modeling and analysis (analytical or simulation-based)
- Computer architecture and micro-architecture knowledge
- Deep learning workload profiling on accelerators
- Software engineering fundamentals
- GPU architectures and CUDA programming
- Transformer model inference optimization
- Architecture simulators (gem5, trace-driven tools)
- ASIC/FPGA/CGRA accelerator development
Benefits
- Medical, dental, and vision coverage with $500/month credit option
- $2,000/month housing subsidy for those within walking distance of office
- Relocation support to San Jose
- Wellness benefits including fitness and mental health
- Daily lunch and dinner provided
- Unlimited compute budget subject to ROI justification
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Etched
Etched builds AI inference hardware, designing and manufacturing complex mechanical and electrical components for data center systems. The company is hiring mechanical engineers, PCB designers, hardware verification engineers, and software engineers to develop manufacturing processes, circuit boards, verification infrastructure, and internal tools across its hardware and operations.
- Website
- etched.com
Likely interview questions
- Walk us through your experience building performance models—were they analytical, simulation-based, or both? How did you validate them against real hardware?
- Describe a time you profiled a deep learning workload on an accelerator (GPU, TPU, ASIC, etc.) and identified a micro-architectural bottleneck. What did you do with that insight?