Director, Machine Learning Engineering - GAI Search Platform - Moveworks

ServiceNow•Mountain View, CA

6h•Onsite

About The Position

As the leader of a team of talented machine learning engineers, your foremost objective will be to establish a ML platform facilitating AI-powered enterprise search applications leveraging large language models (LLMs). You will guide a team in architecting the ranking systems, developing a framework, and providing tools to ensure system health, while also solidifying model training and evaluation workflows. Your team will occupy a pivotal role connecting search infrastructure with ML-based ranking, and will ultimately be accountable for the stability and performance of enterprise search products built on top of the platform. Your team's ownership of the search platform is crucial to the company's search product lines, with success measured by its enablement capabilities. This platform should facilitate rapid iteration on model enhancements for relevance engineers, allowing them to improve ML metrics with a clear understanding of performance tradeoffs. Additionally, it should enable product engineers to develop novel search applications with ease. 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 relevance engineers, and partner with product, design, and customer success teams to jointly achieve business objectives.

Requirements

Master's degree in Computer Science or a related field. A Ph.D. is a plus.
8+ years of experience in machine learning or software engineering, 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.

Responsibilities

Team Leadership: Recruit, hire, and mentor a high-performing team of machine learning engineers.
Maintain a “system focus” mindset in your team.
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.
Advocate for platform usability by gathering feedback from internal relevance and product engineers, and ensuring their workflows are fully supported.