微软Senior Applied Scientist (Bing Ads)
任职要求
Bachelor, Master, PhD degree in CS/EE or related areas with at least 3-year working experience.Good design and problem-solving skills to handle challenging tasks independently.Excellent self-learning ability to try new ideas from textbooks or research papers and apply them to real business scenario of the assigned tasks.Self-motivated with strong passion on machine learning and related tasks.Experiences on search ads recall, relevance & ranking system, LLM is a good plus.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.工作职责
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.
• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.
• Apply state-of-the-art research and advanced algorithms to develop scalable, data-driven solutions that deliver measurable product impact. • Collaborate with product, engineering, and research teams to transfer technology and integrate innovative approaches into production systems. • Design and optimize machine learning models and data pipelines for large-scale applications, ensuring performance, scalability, and ethical standards. • Mentor and guide less experienced team members, fostering technical growth and sharing best practices across projects. • Stay current with industry trends and emerging technologies; publish research and share insights to advance innovation and business impact. • Incorporate fairness, bias detection, and privacy considerations into research and product development processes. • Drive improvements in data quality and leverage advanced analytics to identify opportunities for product enhancement.
• Design and implement offline evaluation strategies that capture real-world usage and reflect end-user preferences. • Develop scientifically sound metrics that diagnose model regressions, benchmark against baselines (e.g., ChatGPT, Glean), and validate product improvements. • Manufacture synthetic yet realistic user activity data using LLMs to simulate diverse usage scenarios. • Collaborate on multi-agent systems or agentic workflows to automate evaluation flows and generate high-signal insights. • Analyze evaluation outputs to identify gaps in coverage, quality, and usability across Copilot canvases. • Partner with engineering and PMs to ensure insights are integrated into product workflows and experimentation pipelines. • Publish learnings in internal forums, external conferences, and contribute to best practices in applied science.