Senior Engineering Manager (AI Search & Retrieval)

About the Team: AI Search & Retrieval

The AI Search & Retrieval team owns our core search platform, serving hundreds of millions of queries per day across enterprise customers. We build and operate highly scalable, low-latency systems that combine traditional information retrieval with modern ML-driven ranking, semantic understanding, and large-scale data processingall in production at scale.

This team sits at the intersection of systems engineering, search relevance, and applied ML, with a direct impact on customer revenue and experience.

The Role

As a Senior Engineering Manager for AI Search & Retrieval, you will lead a high-performing team of engineers (including Senior Staff level talent) responsible for shaping the architecture and long-term evolution of our Search platform. You are a proven engineering leader who is passionate about mentoring technical talent, fostering a culture of psychological safety, and rallying a team to deliver on complex technical initiatives that directly contribute to business goals.

This role is ideal for someone who balances strong people leadership with deep technical empathy for Search domains, distributed systems, and modern AI/ML production systems.

What Youll Do

Technical Leadership & Domain Strategy

• Evolve Search Architecture: Champion the technical roadmap and long-term strategy for core Search infrastructure, spanning indexing, retrieval, ranking, query-time execution, and data processing.
• Bridge Engineering & Applied Science: Partner closely with Applied Science and ML teams to productionize advanced ranking models, vector embeddings, and semantic retrieval techniques.
• System Excellence at Scale: Ensure search systems meet strict latency, availability, and cost-efficiency SLAs under heavy, real-world enterprise traffic.
• Modernize Data Pipelines: Direct the implementation of scalable data streaming and ingestion platforms to ensure search indexes are accurate, automated, and optimized.

Team and Project Management

• Execute with Predictability: Collaborate closely with Product Management and cross-functional stakeholders to translate ambiguous business needs into clear, actionable technical roadmaps.
• SDLC & Operational Health: Manage the full project lifecycle using structured development cycles. Set high expectations for code quality, rigorous design reviews, change management, and search evaluation metrics (including A/B testing).
• Global Collaboration: Drive alignment and consensus across geographically distributed teams and time zones, proactively resolving dependencies and architectural conflicts.

People Leadership & Culture

• Nurture High-Caliber Talent: Build, hire, and mentor a best-in-class search engineering team, fostering an inclusive culture of continuous learning, technical excellence, and high performance.
• Coach Technical Leaders: Act as a manager and career coach to senior technical talent (including Staff/Senior Staff engineers), creating growth opportunities, defining clear career paths, and helping them maximize their impact as multipliers.

What Youll Bring

Technical Depth & Experience

• 8+ years of professional software engineering experience, with a proven track record in backend infrastructure, distributed systems, and large-scale computing.
• Strong domain expertise in Search or Information Retrieval systems, including understanding of search relevance, ranking, and catalog indexing.
• Deep data ecosystem knowledge: Experience with big data processing or streaming technologies (e.g., Spark, Kafka, Flink, Solr, Lucene, or Elasticsearch).
• Cloud Operations: 4+ years of hands-on experience architecting and operating production systems within AWS, GCP, or another major cloud provider.
• Language Proficiency: Strong foundations in core production languages, with Java and/or Python strongly preferred.

Leadership & Product Acumen

• 4+ years of experience successfully leading, hiring, coaching, and developing high-performance engineering teams.
• Strategic Vision: Ability to possess and articulate a strong product and architectural vision for AI-driven platforms, balancing precision, recall, latency, and operational costs.
• Ambiguity to Execution: Proven ability to handle complex, high-volume production environments with excellent prioritization, time management, and a focus on measurable execution.
• Communication: Exceptional communication skills, with the ability to explain complex technical tradeoffs to business stakeholders and advocate for engineering health.

Bonus Points

• Hands-on experience with vector search, embeddings, or semantic retrieval.
• Familiarity with offline search evaluation frameworks and online A/B experimentation.
• Experience in a high-growth, fast-paced SaaS, e-commerce search, or startup environment.

What Success Looks Like in the First 30/60/90 Days

First 30 Days: Immersion & Foundation

• Cultural & Process Acumen: Achieve a deep understanding of our core company values, the development lifecycle, and quality/release processes.
• Product Architecture Mastery: Develop a high-level comprehension of the AI Search & Retrieval architecture, its key components, and underlying data flows.
• Team & Project Mapping: Establish trusted working relationships with all team engineers. Gain a clear handle on all in-flight search initiatives, priorities, and individual ownership.

First 60 Days: Ownership & Optimization

• Deep Component Knowledge: Attain a thorough understanding of search relevance systems, upstream/downstream dependencies, and how the platform delivers customer business value.
• Process Ownership: Fully own and optimize the teams operating norms and processes (e.g., sprint planning, bug triage, query performance review, and on-call rotations).
• Internal Network: Establish key, cross-functional working relationships with Product, Applied Science, and Engineering leaders outside the immediate team.

First 90 Days: Strategic Impact & Talent Development

• Operational Credibility: Successfully shepherd a small feature, enhancement, or optimization through to production deployment, validating system and process knowledge.
• Team & Quality Assessment: Develop a clear, objective insight into the team's technical strengths, weaknesses, and key skill or capacity gaps.
• Strategic Improvement Plan: Collaborate with Product and Engineering leadership to propose concrete suggestions for improving search relevance, system latency, or team efficiency.
• Talent Management Kick-off: Actively manage engineers' careers, establishing robust rapport and defining tailored growth opportunities for each team member.