Etched

8 months ago

Performance Modeling Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Performance Engineer role focused on modeling and optimizing transformer inference workloads across diverse architectures. You’ll develop analytical and simulation-based performance models, profile deep learning workloads on accelerators, collaborate with hardware teams on micro-architectural decisions, and drive cross-layer optimization to maximize efficiency from circuits to workloads in a fully in-person San Jose team.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop performance models and projections for transformer-specific architecture across workloads and configurations
•Profile and analyze deep learning workloads on hardware accelerators to identify micro-architectural bottlenecks
•Build analytical and simulation-based models to predict performance under various architectural configurations and trade-offs
•Collaborate with hardware architects to inform design decisions based on workload characteristics and performance analysis
•Drive hardware/software co-optimization by identifying features that unlock significant performance improvements

Pay and Benefits

Salary: USD 175,000 - 275,000 annually

Perks:MedicalDentalVisionHousing SubsidyRelocation SupportWellness StipendMeal Allowance

Key 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:Hardware accelerationTransformersAI accelerators

Skills:Problem-solvingAnalytical thinkingCommunicationTeam collaboration

Tech Stack:GPUCUDAASICFPGAGem5

Company Brief

Etched

Designs transformer‑specialized AI inference ASICs (product: Sohu) to accelerate large‑language‑model workloads, working with TSMC for fabrication and targeting energy‑efficient inference performance.

Industry: Hardware Devices

Company Size: Medium (51 to 250 employees)

Growth: Early Stage Startup

Valuation: Unicorn (USD 1B+)

Funding: Series A

Headquarters: San Jose, United States

Founded: 2022

Website LinkedIn Glassdoor

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Etched

8 months ago

Performance Modeling Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop performance models and projections for transformer-specific architecture across workloads and configurations
•Profile and analyze deep learning workloads on hardware accelerators to identify micro-architectural bottlenecks
•Build analytical and simulation-based models to predict performance under various architectural configurations and trade-offs
•Collaborate with hardware architects to inform design decisions based on workload characteristics and performance analysis
•Drive hardware/software co-optimization by identifying features that unlock significant performance improvements

Pay and Benefits

Salary: USD 175,000 - 275,000 annually

Perks:MedicalDentalVisionHousing SubsidyRelocation SupportWellness StipendMeal Allowance

Key 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:Hardware accelerationTransformersAI accelerators

Skills:Problem-solvingAnalytical thinkingCommunicationTeam collaboration

Tech Stack:GPUCUDAASICFPGAGem5

Company Brief

Etched

Industry: Hardware Devices

Company Size: Medium (51 to 250 employees)

Growth: Early Stage Startup

Valuation: Unicorn (USD 1B+)

Funding: Series A

Headquarters: San Jose, United States

Founded: 2022

Website LinkedIn Glassdoor