Etched
AI
InferenceInternSummer2027
“Inference Intern - Summer 2027 at Etched. Skills: AI inference system, transformers, compute architectures, performance modeling, model porting, runtime optimization, 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, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning reasoning agents; redefining the infrastructure layer for the fastest growing industry in history; deliver exceptional performance and efficiency for transformer workloads; maximize model performance
Industry & Context.
architectural problems; performance modeling; bottlenecks; correctness issues
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
fully in-person team; no boundaries between engineering and research; expect all of our technical staff to contribute to both as needed
Applying for this Inference Intern - Summer 2027 role?
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