Architecture Intern - Inference

Department: Architecture

Location: San Jose

Employment Type: Intern

About Etched

Etched is building the world’s first AI inference system purpose-built for transformers - delivering over 10x higher performance and dramatically lower cost and latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. 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.

Job Summary

We are seeking a talented Architecture intern to join our team and contribute to the design of next-generation AI accelerators. This role focuses on developing and optimizing compute architectures that deliver exceptional performance and efficiency for transformer workloads. You will work on cutting-edge architectural problems and performance modeling over the course of your internship.

Key responsibilities

• Support porting state-of-the-art models to our architecture. Help build programming abstractions and testing capabilities to rapidly iterate on model porting.

• Assist in building, enhancing, and scaling Sohu’s runtime, including multi-node inference, intra-node execution, state management, and robust error handling.

• Contribute to optimizing routing and communication layers using Sohu’s collectives.

• Utilize performance profiling and debugging tools to identify bottlenecks and correctness issues.

• Develop and leverage a deep understanding of Sohu to co-design both HW instructions and model architecture operations to maximize model performance

• Implement high-performance software components for the Model Toolkit

You may be a good fit if you have

• Progress towards a Bachelor’s, Master’s, or PhD degree in computer science, computer engineering, applied mathematics, or a related field

• Proficiency in Python, C++

• Understanding of performance-sensitive or complex distributed software systems, e.g. Linux internals, accelerator architectures (e.g. GPUs, TPUs), Compilers, or high-speed interconnects (e.g. NVLink, InfiniBand).

• Ported applications to non-standard accelerator hardware or hardware platforms.

• Deep knowledge of transformer model architectures and/or inference serving stacks (vLLM, SGLang, etc.)

Strong candidates may have some experience with

• Proficiency in Rust

• Low-latency, high-performance applications using both kernel-level and user-space networking stacks.

• Deep understanding of distributed systems concepts, algorithms, and challenges, including consensus protocols, consistency models, and communication patterns.

• Solid grasp of Transformer architectures, particularly Mixture-of-Experts (MoE).

• Built applications with extensive SIMD (Single Instruction, Multiple Data) optimizations for performance-critical paths.

• Familiarity with PyTorch or JAX.

• Math competitions (AIME, AMC, etc)

We encourage you to apply even if you do not believe you meet every qualification.

Program details

• 12-week paid internship (June - August 2026)

• Generous housing support for those relocating

• Daily lunch and dinner in our office

• Based at our office in San Jose, CA

• Direct mentorship from industry leaders and world-class engineers

• Opportunity to work on one of the most important problems of our time

For any questions, contact [email protected].

How we’re different

Etched believes in the Bitter Lesson. We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.

We are a fully in-person team in West San Jose, 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 as needed.