- Where
- San Jose
- Posted
- Dec 8, 2025
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.
Job Summary
We are seeking talented Fall '26, Spring '27, and Summer '27 Inference Architecture interns 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 inference workloads. You will work on cutting-edge architectural problems and performance modeling over the course of your internship.
What you'd do
- 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 our runtime, including multi-node inference, intra-node execution, state management, and robust error handling.
- Contribute to optimizing routing and communication layers using our collectives.
- Utilize performance profiling and debugging tools to identify bottlenecks and correctness issues.
- Develop and leverage a deep understanding of our architecture to co-design both HW instructions and model architecture operations to maximize model performance
- Implement high-performance software components for the Model Toolkit
- 12-week paid internship
- Generous housing support for those relocating
- Daily lunch and dinner in our office
- Based at our office in San Jose, CA
What they want
- 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.)
- 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.