Inference Intern

The Consensus; San Jose (CA); On-site; USD 55,104 - 82,656; Part time

Benefits offered by this job

12-week paid internship

Housing support for relocation

Daily lunch and dinner in office

Direct mentorship from industry leads

Office in San Jose, CA

Job summary

Etched in San Jose, CA is offering a 12-week paid internship in Inference Architecture for Fall '26, Spring '27, and Summer '27. Candidates will contribute to designing and optimizing AI accelerator compute architectures and performance for inference workloads.

You will port models, build testing abstractions, and help scale our runtime, with mentorship from industry leaders and hands-on work on cutting-edge architectural problems. Relocation housing support and daily meals are provided.

Qualifications

• Pursuing a Bachelor's, Master’s, or PhD in CS, CE, applied mathematics, or related field.
• Proficiency in Python and C++.
• Understanding of performance-sensitive distributed software systems.

Responsibilities

• Port models to our architecture and build testing capabilities.
• Help build, scale, and maintain the runtime for multi-node inference and state management.
• Optimize routing and communication layers using collectives.
• Use profiling tools to identify bottlenecks and correctness issues.
• Co-design HW instructions and model architecture operations to maximize performance.
• Develop high-performance software components for the Model Toolkit.

Skills

Python

C++

Education

Bachelor’s/Master’s/PhD candidate

Tools

PyTorch

JAX

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.

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.

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 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

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

• 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 [e-mail].

How we’re different

Etched believes in the Bitter Lesson. We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. 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.