Performance Engineer at Etched.ai

San Jose, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI Hardware, SemiconductorsIndustries

Requirements

Deep expertise in computer architecture and micro-architecture, particularly for accelerators or domain-specific architectures
Strong performance modeling and analysis skills with experience building analytical or simulation-based performance models
Experience profiling and optimizing deep learning workloads on hardware accelerators (GPUs, TPUs, ASICs, FPGAs)
Strong understanding of hardware/software co-design principles and cross-layer optimization
Solid foundation in digital circuit design and how micro-architectural decisions impact performance
Experience with reconfigurable or heterogeneous architectures
Ability to reason quantitatively about performance bottlenecks across the full stack from circuits to workloads
Strong candidates may also have
PhD or equivalent research experience in Computer Architecture or related fields
Experience with ASIC, FPGA, or CGRA-based accelerator development
Published research in computer architecture, ML systems, or hardware acceleration
Deep knowledge of GPU architectures and CUDA programming model
Experience with architecture simulators and performance modeling tools (gem5, trace-driven simulators, custom models)
Track record of informing architectural decisions through rigorous performance analysis
Familiarity with transformer model architectures and inference serving optimizations

Responsibilities

Develop comprehensive performance models and projections for Sohu's transformer-specific architecture across varying workloads and configurations
Profile and analyze deep learning workloads on Sohu to identify micro-architectural bottlenecks and optimization opportunities
Build analytical and simulation-based models to predict performance under different architectural configurations and design trade-offs
Collaborate with hardware architects to inform micro-architectural decisions based on workload characteristics and performance analysis
Drive hardware/software co-optimization by identifying opportunities where architectural features can unlock significant performance improvements
Characterize and optimize memory hierarchy performance, interconnect utilization, and compute resource efficiency
Develop performance benchmarking frameworks and methodologies specific to transformer inference workloads

Skills

performance modeling

profiling

deep learning workloads

micro-architectural analysis

simulation models

roofline models

memory hierarchy

interconnect utilization

compute efficiency

benchmarking frameworks

transformer inference

hardware/software co-optimization

Llama

Mixtral

Etched.ai

Develops servers for transformer inference

About Etched.ai

The company specializes in developing powerful servers for transformer inference, utilizing transformer architecture integrated into their chips to achieve highly efficient and advanced technology. The main technologies used in the product are transformer architecture and advanced chip integration.

Cupertino, CA, USAHeadquarters

2022Year Founded

$5.4MTotal Funding

SEEDCompany Stage

HardwareIndustries

11-50Employees