
Performance Modeling Engineer at Etched (San Jose, CA)
Etched· San Jose, CA·
Role details
Etched at a glance
Transformer-specific ASICs and rack-scale systems that hardwire the transformer into silicon for faster, cheaper AI inference.
Etched designs transformer-specific ASICs and full rack-scale inference systems. Its Sohu chip hardwires the transformer architecture directly into silicon, trading a GPU's general-purpose flexibility for far higher throughput, lower latency, and better power efficiency on AI inference. Customers buy complete co-designed clusters, chip plus rack plus software, not standalone chips.
$800M raised · latest: $500M · led by Stripes · Dec 2025 (reported Jan 2026) · backed by Stripes, Peter Thiel, Ribbit Capital, VentureTech Alliance
Job description
About Etched
Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.
Key responsibilities
- Develop comprehensive performance models and projections for our architecture across varying workloads and configurations
- Profile and analyze deep learning workloads on our hardware to identify micro-architectural bottlenecks and influence optimization opportunities
- Drive hardware/software co-optimization by identifying where architectural features can unlock performance improvements
- Run regressions and validate performance models against real systems and silicon
- Inform next-generation architectural decisions by pathfinding across system and silicon options during design, proof-of-concept, and architecting phases
You may be a good fit if you have at least one of the following:
- Strong performance modeling and analysis skills with experience building analytical-based or simulation-based performance models
- Solid understanding of computer architecture and micro-architecture, particularly for accelerators
- Experience profiling and analyzing deep learning workloads on hardware accelerators (GPUs, TPUs, ASICs, FPGAs, or others)
- Solid software engineering fundamentals with an eye toward auditability and maintainability
Strong candidates may also have
- Deep knowledge of GPU architectures and/or programming models like CUDA
- Experience mapping models to multi-chip inference systems
- Familiarity with transformer model architectures and inference serving optimizations
- Experience with architecture simulators and performance modeling tools (gem5, trace-driven simulators, custom models)
- Exposure to ASIC, FPGA, or CGRA-based accelerator development and hardware/software co-design principles
- Published research in computer architecture, ML systems, or hardware acceleration
Benefits
- Medical, dental, and vision packages with generous premium coverage
- $500 per month credit for waiving medical benefits
- Housing subsidy of $2.5k per month for those living within walking distance of the office
- Relocation support for those moving to San Jose (Santana Row)
- Various wellness benefits covering fitness, mental health, and more
- Daily lunch + dinner in our office
- Unlimited compute budget subject to ROI justification
How we’re different
Etched believes in the Bitter Lesson. We are the first inference-focused frontier AI system. Our addressable market is the entirety of inference, unlike many of our competitors.
We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.
Why work at Etched
- Mission-driven, high-impact engineering: “Building the hardware for superintelligence” – the company is solving one of the hardest problems in compute (inference efficiency) with a focused team.
- Top-tier team and learning environment: Colleagues from Google, NVIDIA, Apple, Tesla, Broadcom, and Intel; exposure to full-stack chip-to-system design.
- In-office culture: All roles are in-office (Cupertino and San Jose, CA). The company emphasizes on-site collaboration for hardware development. Notable perks are not heavily advertised, but compensation is competitive for the AI hardware space (equity-heavy).
- Growth trajectory: 141% headcount growth YoY, hundreds of open roles, and a $5B valuation signal strong backing and career acceleration potential.
- Intellectual challenge: Work on chip simulation, RTL design, system validation, or software infrastructure for cutting-edge AI inference.