Senior Manager of Machine Learning Engineering

Join our team as a Senior Manager of Machine Learning Engineering. In this role, you will lead and grow a team of software engineers and machine learning engineers, driving the strategy, architecture, and execution for search, ranking, relevance, personalization, and RAG capabilities. You will partner with product managers and architects to define and deliver innovative AI-powered experiences, build scalable retrieval pipelines, and improve overall search quality. Additionally, you will mentor engineering leaders and individual contributors, evaluate emerging AI technologies, and balance short-term delivery goals with long-term technical investments.

• Lead and grow a team of software engineers and machine learning engineers to build large-scale AI search and retrieval systems.
• Drive the strategy, architecture, and execution for search, ranking, relevance, personalization, and RAG capabilities.
• Partner with product managers and architects to define and deliver innovative AI-powered experiences.

• 12+ years of software engineering experience with a Bachelor's degree, or 10+ years with a Master's degree, or equivalent
• Hands-on experience with search technologies such as Lucene, Elasticsearch, Solr, OpenSearch, Algolia, Coveo, Glean, or similar platforms
• Knowledge of cloud-native architectures, microservices, observability, and performance optimization
• Experience building and operating large-scale retrieval, ranking, recommendation, or search systems
• Strong collaboration and communication skills with the ability to influence across engineering and product organizations
• Strong understanding of information retrieval, search relevance, ranking algorithms, and semantic search
• Strong expertise in Java or Python and large-scale distributed systems
• 3+ years of engineering management experience leading high-performing technical teams
• Experience deploying and operating machine learning models in production environments
• Proven track record of delivering innovative products from concept through production at enterprise scale
• Experience with modern AI/ML technologies, including LLMs, embeddings, vector databases, and RAG architectures
• Experience with personalization, recommendation systems, or relevance optimization
• Experience leading geographically distributed engineering teams
• Experience working with vector search technologies and embedding frameworks
• Experience building enterprise search, knowledge retrieval, or GenAI products
• Experience integrating AI capabilities into customer-facing SaaS products