Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA
A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment
Strong engineering skills, including experience in developing large distributed systems or high-load web services
Open-source projects that showcase your engineering prowess
Excellent command of the English language, alongside superior writing, articulation, and communication skills
A profound understanding of theoretical foundations of machine learning and transformer architecture
Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
Understanding of GPU memory hierarchy and compute/memory tradeoffs
Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
Strong software engineering skills (we mostly use Python)
Deep experience with modern deep learning frameworks
Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
Strong communication and leadership abilities