微软Applied Scientist 2

Model Optimization & Deployment: Design and implement efficient workflows for training, distillation, and fine-tuning Small and Large Language Models (SLMs), leveraging techniques such as LoRA, QLoRA, and instruction tuning. Apply model compression strategies—including quantization (e.g., GPTQ, AWQ) and pruning—to reduce inference costs and improve latency. Optimize LLM inference performance using frameworks like vLLM and TensorRT-LLM (TRT-LLM) to enable scalable, low-latency deployment. Build robust and scalable inference systems tailored to heterogeneous production environments, with a strong focus on performance, cost-efficiency, and stability. Evaluation & Data Management： Develop evaluation datasets and metrics to assess model performance in real-world product scenarios. Build and maintain end-to-end machine learning pipelines encompassing data preprocessing, training, validation, and deployment.  Cross-functional Collaboration： Collaborate closely with product managers, engineers, and research scientists to translate business needs into impactful AI solutions, driving real-world adoption and seamless product integration.

• Bridges the gap between research and development teams to bring state-of-the-art technologies—especially in NLP, LLMs, and recommender systems—into Microsoft products, driving immediate product impact. • Designs, develops, and optimizes ranking and recommendation algorithms (e.g., for News & Feeds), incorporating advanced machine learning and generative AI techniques to improve user engagement and product features. • Performs deep data analysis to uncover patterns and trends, builds systemic models to enhance recommendation relevance and user experience, and prepares high-quality datasets for ML applications. • Applies and improves ML algorithms, prepares and adapts data for scalable AI solutions, and supports deployment, monitoring, and iteration of models in production environments. • Works with internal and external product and business groups, contributes to technology transfer, patents, and white papers, and consults on applying advanced concepts to real-world product needs. • Maintains academic and professional networks, supports recruiting and mentoring efforts, and contributes to publications and internal knowledge sharing to foster innovation and talent development. • Documents research processes and findings, adheres to ethics and privacy standards, and aligns work with team strategy while gaining expertise in key research domains like machine learning, computer vision, and statistical modeling.

A good candidate will play a key role in driving algorithmic and modeling improvement to the system, analyze performance and identify opportunities based on offline and online testing, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads. The candidate should also have good communication, collaboration, and analytical skills.

职位：Applied scientist 应用科学家实习生 毕业时间：2026年10月 - 2027年7月之间毕业的应届毕业生 · 入职日期：2026年6月及之前 · 实习时间：保证一周实习4-5天全职实习，至少持续3个月 · 工作地点：深圳福田区 投递须知： 1 填写简历申请时，请把必填和非必填项都填写完整。提交简历之后就无法修改了哦！ 2 学校的英文全称请准确填写。中英文对应表请查这里（无法浏览请登录后浏览）https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2 如果您正在攻读计算机，AI，ML领域专业的博士或硕士研究生，而且对应用科学家的实习工作感兴趣。如果您也喜爱深入研究棘手的技术问题并提出解决方案，用成功的产品显著地改善人们的生活。 那么，我们诚挚邀请您加入亚马逊。这会是一份收获满满的工作。您每天的工作都与全球数百万亚马逊客户的体验紧密相关。您将提出和探索创新，基于TB级别的产品和流量数据设计机器学习模型。您将集成这些为客户提供服务，通过数据，建模和客户反馈来完成闭环。您对模型的选择需要能够平衡业务指标和响应时间的需求。