VP of AI Search & Recommendations

Search is the front door to Realtor.com.

Your mandate: make our AI search & discovery experience the most trusted, most relevant, and fastest path to the right home—and do it at public-company scale. As VP of Engineering, AI Search & Discovery, you’ll lead a multidisciplinary organization across AI Search Engineering, Relevance/Ranking, Query Understanding, Personalization, and ML Platforms, partnering tightly with Product, Data, and Revenue to move the company’s most important customer and business metrics.

This is a hands-on VP role: you’ll define the strategy, shape the architecture, hire and develop the leaders, and hold a very high bar on execution, reliability, and measurable outcomes.

What You'll Do

Set the bar and the direction

• Own the 3 year technical strategy for AI search, discovery, and recommendations; align it to company OKRs and News Corp governance.
• Establish a metrics-first culture: nDCG/ERR, CTR, lead quality, P50/P95 latency, experiment velocity, and infra cost per search.

Build & lead a world-class engineering organization

• Lead the team across core search, ranking, query understanding, personalization, and ML platform; grow and coach an org spanning software engineers, relevance scientists, and ML engineers.
• Raise the talent bar: recruit senior technical leaders and principal-level ICs; set clear career paths and performance standards.

Own the search stack end-to-end

• Retrieval & indexing: hybrid vector + semantic, data quality, dedupe, freshness, and geo/local considerations.
• Ranking: multi-objective LTR (relevance, quality, monetization), session & user modeling, cold-start handling for new listings, image and text quality signals, and advanced re-ranking (including LLM-assisted ranking where it wins).
• Query understanding: intents, entities, spell-correction, reformulation, facets, structured constraints, ambiguity resolution for neighborhoods/communities.
• Personalization: embeddings & features, eligibility/guardrails, unbiased evaluation, and robust online/offline experimentation pipelines.
• Evaluation & experimentation: offline suites, interleaving, online A/B, sequential testing; ship improvements continuously with safety rails.

Modernize the platform

• Shape our ML & inference platform for low-latency ranking and re-ranking
• Partner with platform teams on SLOs, cost, capacity planning, observability, and incident response (you’ll expect clean postmortems and durable fixes).
• Advance pragmatic GenAI: RAG for search assistance, answer synthesis with citations, robust eval harnesses (task-specific metrics, human-in-the-loop), and cost/perf optimization.

Operate with rigor at a public company

• Work with Legal/Privacy/Compliance on MLS data obligations, Fair Housing considerations, and AI governance; ensure robots/agents compliance and abuse/spam defenses.
• Communicate with executive leadership and, when needed, Boards/partners; convert complex trade-offs into crisp, defensible decisions.

• Proven scale: You’ve led search/recommendation systems serving tens of millions of users or equivalent high-stakes marketplaces, with measurable lifts in relevance and revenue.
• Depth in search & ML: 12+ years in large-scale search/recommendation, including hybrid retrieval, learning-to-rank, user modeling, and evaluation. You’ve shipped multi-objective ranking safely in production.
• GenAI pragmatism: Hands-on leadership integrating LLMs where they improve outcomes (e.g., re-ranking, query reformulation, answer synthesis) with strong offline/online evaluation discipline.
• Platform and operations chops: Real-time inference, feature stores, streaming data, strong SRE partnership, and a habit of designing for latency, reliability, and cost.
• People & org leadership: Built and scaled multi-disciplinary teams; hired/developed Directors and Principals; set culture, strategy, and execution cadence.
• Executive communication: Clear storyteller able to align product, design, science, finance, legal, and revenue stakeholders on trade-offs and timelines.
• Austin presence: Willing and able to work onsite at our Austin HQ (relocation required to make Austin your primary residence). 

• Core Search (indexing, retrieval, query understanding)
• Ranking & Relevance Science (features, models, re-ranking, guardrails)
• Personalization (session/user modeling, eligibility, privacy-aware features)
• Evaluation & Experimentation (offline/online, human-in-the-loop)
• ML/Inference Platform for search use-cases (latency-critical APIs, feature store)
• Quality & Integrity (spam/fraud/abuse defenses; content quality signals)

• Quality: Meaningful gains in nDCG/ERR and conversion/lead quality; improved first-result time and P95 latency on key surfaces.
• Velocity: 2–3× increase in experiment throughput; reliable weekly relevance drops with automated evaluation.
• Platform: Real-time re-ranking and feature pipeline in place; safe & measurable GenAI integrations where they outperform baselines.
• Engineering Organization: Fully staffed leadership bench; clear succession and hiring plan; healthy engagement and retention.
• Operational excellence: SLOs met, sharp incident posture, and a cost curve that bends the right way.