OverviewAre you an experienced backend engineer with a passion for building large scale distributed systems to enable more innovations on the search, recommendation, and AI services and products. Are you passionate about working as a tech leader to architect and drive cutting-edge techniques such as LLM, Ranking, Index Serving in large scale like 100K+ nodes by collaborating with ML/AI data scientists? Bing IndexServe team have a Principal Architect position to meet your technical expertise.Bing Fundamentals focuses on providing a search and recommendation platform for Microsoft internal partners using the documentation recall and reranking backend service suite. As a team, we manage one of the world’s largest distributed systems and have experienced great success in building efficient large scale distributed systems for search. Now, the team has a per decade rare opportunity to simplify the serving stack, to serve the biggest index in the most efficient way, and to drive up the relevance innovations with advanced deep learning and Large Language model techniques. The agility of engineering and deployment also presents as a big challenge when handling such a complex system. As Bing embraces the newest LLM innovations, the need for IndexServe to build up AI intelligence is becoming bigger.Within Bing Fundamentals, we are the IndexServe team, and we have the exciting responsibility of trying to tackle these challenges. As a team, we are trying to build the most agile, performant, stable, experientable yet efficient index serving platform. On this platform, relevance techniques can be quickly implemented, iterated, qualified and flighted to evaluate their customer impacts with convenience while full-funnel debuggability. The same platform also provides all the cutting edged techniques and utilities, like deep learning, machine learning, LLM, C# rankers. It has the most advanced AI toolset for our scientists to innovate, and for our products to serve the most relevant documents. #MAI#*Please include the video*
ResponsibilitiesDrives the improvement of artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC). Proactively takes responsibility for the content of their AI-generated requirements, design documents, code, and other assets, assisting the rest of the team to do the same. Incorporates Responsible AI practices into the SDLC to ensure appropriate controls over AI-generated assets. Intentionally applies SDLC and engineering health measures (e.g., Accelerate, SPACE framework, Engineering System Success Playbook [ESSP]) to guide improvements to processes and practices, especially those involving AI. Experiments with AI tools and practices to improve their own capabilities, and provides recommendations on how to adopt them to the rest of the team.
Provides technical leadership during code reviews for a solution/product area to assure it meets team standards, contains the correct test coverage, and is appropriate for the product or solution area. Brings expertise to code reviews to help improve code quality, proactively coaching and providing feedback to develop other engineers' skills. Ensures coding standards are followed. Screens for and establishes best practices in reviews and provides feedback on code to drive adherence to best practices. Uses automated source code analysis tools that are incorporated into the build/development process.
Leads by example across teams and mentors others to produce extensible, maintainable, well-tested, secure, and performant code used across the company that adheres to design specifications. Leads efforts to continuously improve code performance, testability, maintainability, effectiveness, and cost, while accounting for and incorporating relevant trade-offs. Identifies best practices and coding patterns (e.g., leveraging state-of-the-art generative artificial intelligence [GenAI], approaches to source code organization, naming conventions) and provides deep expertise in the coding and validation strategy. Creates and applies metrics to drive code quality and stability, appropriate coding patterns, and best practices. Leads efforts to identify and anticipate blockers or unknowns during the development process, escalate them, and communicate how they will impact timelines, and then drives the identification and implementation of strategies and/or opportunities to address them.
Acts as an expert on using debugging tools, tests, logs, telemetry, and other methods, and proactively leads verification of assumptions through while developing code before issues occur across products and teams in production. Leverages minimal telemetry data, triangulates issues, and resolves with minima