Etched
AI
InferenceIntern-Spring2027
Neural analysis suggests this role is
optimal for Entry candidates.
“Inference Intern - Spring 2027 at Etched. Skills: AI inference system design, transformer workloads, performance modeling, model porting, runtime development, Python, C++, accelerator architectures, distributed systems. Support porting state-of-the-art models to our architecture. Help build programming abstractions and testing capabilities to rapidly iterate on model porting”
What You'll Achieve.
delivering over 10x higher performance and dramatically lower cost and latency than a B200; build products that would be impossible with GPUs; maximize model performance
Industry & Context.
architectural problems; performance modeling; identify bottlenecks; correctness issues; co-design both HW instructions and model architecture operations to maximize model performance
fully in-person team
What They're Looking For.
Must 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. )
Nice to Have
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)
What You'll Do.
Support porting state-of-the-art models to our architecture
Help build programming abstractions and testing capabilities to rapidly iterate on model porting
and scaling Sohu’s runtime
including multi-node inference
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
How You'll Work.
Team & Collaboration
Direct mentorship from industry leaders and world-class engineers; fully in-person team; no boundaries between engineering and research; expect all of our technical staff to contribute to both as needed
Full Job Description
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 talented fall or winter 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 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 sy
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