Practical Insights on Transitioning into an ML Systems Engineer
sharpeye_wnl · x · 2026-07-11
The sharer outlines some of the representative work they did over the past 6-8 months while transitioning into ML systems / AI infra:
- A Python inference engine for testing inference technologies, achieving around 600 toks/s on an RTX 4060 Ti when paired with prefix caching.
- A technical article demonstrating how to outperform cuBLAS on Ada using the cute DSL.
- A detailed breakdown of NCCL collective communication.
- A blog series explaining how torch.compile works and its internal mechanisms.
- Continuously organizing personal ML systems notes and experiments, and contributing to open-source projects like SGLang and llm-compressor (vLLM related).
They conclude by asking for advice on what skills to focus on improving in the coming months, with the goal of becoming a stronger ML systems / inference engineer.
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