Job Description

As the leader of a team of talented search relevance engineers, your objective will be to measure and improve the ranking and relevance of our AI-powered enterprise search applications. Since these applications also leverage large language models (LLMs), you will also be responsible for measuring and improving RAG-based objectives such as summarization, groundedness of responses, and citation correctness and completeness. You will guide the team to drive end-to-end development of machine-learning models, including data synthesis, feature engineering, experiment design, evaluation and more. Your team will play a pivotal role in improving our search relevance in a systematic and methodical manner as we scale to new customers, new types of data and use-cases, and will ultimately be accountable for the ranking quality of all our enterprise search products. Your team's ownership of search quality is crucial to the company's search product lines, with success measured by its enablement capabilities. You will enable your team members by facilitating rapid iteration on model enhancements, allowing them to improve ML metrics with a clear understanding of performance tradeoffs and generalizability. You will be responsible for guiding the team's technical direction, managing project timelines, and ensuring the robustness, efficiency, and innovation of our machine learning based search systems. Your team will collaborate closely with search infrastructure and platform engineers, and partner with product, design, and customer success teams to jointly achieve business objectives. What You Will Do: Team Leadership: Recruit, hire, and mentor a high-performing team of machine learning engineers. Maintain a “ranking and relevance” mindset in your team, developing a thought leadership on our long-term relevance roadmap. Foster a collaborative and inclusive team culture, promoting knowledge sharing and continuous learning. Set clear goals, provide regular feedback, and promote professional growth and development of team members. Project Management: Develop and manage project plans, timelines, and budgets for machine learning initiatives. Ensure the successful execution of projects, from ideation and prototyping to production deployment. Collaborate with cross-functional teams to define project requirements and priorities. Technical Leadership: Drive the technical vision and strategy. Guide the integration and application of large language models (LLMs) and retrieval-augmented generation (RAG) techniques to enable modern, intelligent search experiences. Oversee the research, development, and deployment of machine learning models and algorithms. Stay current with the latest advancements in the field and ensure that our projects leverage cutting-edge technologies. Quality and Performance: Implement best practices for model development, data pipelines, and model evaluation. Monitor and optimize the performance, scalability, and reliability of machine learning systems. Ensure that our AI solutions meet high standards of accuracy and efficiency. Stakeholder Communication: Collaborate with leadership, product managers, customer success staff, and other teams to align machine learning initiatives with business goals. Provide regular updates and reports on project status, challenges, and successes to stakeholders. Communicate, collaborate, and build relationships with partner teams and peer teams to facilitate cross-functional projects What you bring to the table: Master's degree in Computer Science specializing in Machine Learning or a related field. A Ph.D. is a plus. 8 years of experience in applied machine learning preferably in the ranking/relevance domain, including 3 years in technical leadership or management roles. Proven technical expertise that has been recognized at Staff Engineer (comparable to Google/Meta L6) or above level. Proven experience managing high-performing teams, including mentoring and supporting Staff-level (I6) or higher engineers, with a strong track record of delivering technically ambitious, production-grade projects. Proficiency in programming languages such as Python, Golang, C++. Excellent problem-solving and analytical skills. Strong communication skills. Knowledge of software engineering best practices and experience with deploying machine learning models in production environments. Compensation Range : $275,000 - $330,000 J-18808-Ljbffr

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