Architecture Intern - Inference
On-site · San Jose, California, United States
Job Summary
The Architecture Intern will support porting state-of-the-art models to architecture, assist in building and scaling runtime features, optimize routing and communication, and utilize profiling tools to identify performance issues. Candidates should have experience and knowledge in computer science or related fields, proficiency in Python and C++, and an understanding of complex software systems and transformer models. The internship offers mentorship from industry leaders, housing support for relocation, and includes daily meals.
Required Qualifications
- 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)
Desired Qualifications
- 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)
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