Follow the latest research on LLMs, inference and source code generation
Propose and evaluate innovations, both in the quality and the efficiency of the inference
Monitor and implement LLM inference metrics in production
Write high-quality high-performance Python, Cython, C/C++, Triton, ThunderKittens, native CUDA, Amazon Neuron code
Work in the team: plan future steps, discuss, and always stay in touch

Experience with Large Language Models (LLM)
Confident knowledge of the computational properties of transformers
Knowledge/Experience with cutting-edge inference tricks
Knowledge/Experience of distributed and lower precision inference
Knowledge of deep learning fundamentals
Strong engineering background
Theoretical computer science knowledge is a must
Experience with programming for hardware accelerators
SIMD algorithms
Expert in matrix multiplication bottlenecks
Know hardware operation latencies by heart
Research experience
Nice to have but not required: Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc
Can freely discuss the latest papers and descend to fine details
You have strong opinions, weakly held
Programming experience
Linux
Git
Python with PyTorch or Jax
C/C++, CUDA, Triton, ThunderKittens
Use modern tools and are always looking to improve
Opinionated but reasonable, practical, and not afraid to ignore best practices
Strong critical thinking and ability to question code quality policies when applicable
Prior experience in non-ML programming is a nice to have

Member of Engineering, Inference

Key skills