微软Applied Scientist 2(Ads)

 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.

• Partner with our Research and PM team to design, develop and ship innovative algorithms and high-quality features to Search Ads system.• Develop a deep understanding of search ads products, apply machine learning, statistic data analysis, computational linguistics, and other technologies to identify areas from web-scale data for major improvements.• Apply SOTA deep learning algorithms and other cutting-edge technologies to build effective and efficient models to improve recall, relevance, and revenue.

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.