Architecture Intern - Inference

Etched•San Jose, CA

30d•Onsite

About The Position

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. 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.

Requirements

Progress towards a Bachelor’s, Master’s, or PhD degree in computer science, computer engineering, or a related field
Proficiency in C++ or Rust.
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).
Familiarity with PyTorch or JAX.
Ported applications to non-standard accelerator hardware or hardware platforms.
Deep knowledge of transformer model architectures and/or inference serving stacks (vLLM, SGLang, etc.)

Nice To Haves

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.

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