Some directions we are currently working on, and which you can be a part of:
Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.
We expect you to have:
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
