budgets. Workwith MLE, GPU kernel, backend infrastructure, product, and customer teams toselect research priorities with measurable production impact. Share results throughinternal reports, technical blogs, papers, and open-sourceartifacts, and mentorengineers and scientists on experimental design, scientific rigor, and model/systemtrade-offs. Must-haves APhD in computer science, machine learning … training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talksdemonstrating contributions toefficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request ...
