Director, Engineering – Title Automation

This is a fully remote position, open to applicants in California.

📋 Description

• Take charge of the architecture and operations of the Data and Document Intelligence Platform.

• Define and enhance a production-ready document intelligence system that efficiently extracts, structures, and governs title insurance document data at scale.

• Oversee the platform that emits title production events to the data lake.

• Lead the development and upkeep of the canonical data model for title production.

• Drive programs for document classification across various document types and geographical areas.

• Collaborate with data science and AI governance teams on labeled datasets, accuracy benchmarks, and evaluation standards.

• Manage engineering programs that expand the Sequoia AI-driven title production platform across different order types, regions, and scenarios.

• Direct the automated generation of title search packages and related outputs, transitioning proof-of-concept programs into full production.

• Define sequencing, investment tradeoffs, and measurable outcomes for automation initiatives.

• Establish architecture and engineering standards across document processing, event-driven data pipelines, data modeling, ML lifecycle, and enterprise AI platforms.

• Create a governed, AI-ready data foundation that includes metadata, lineage, data contracts, lifecycle controls, data quality, and open interfaces.

• Implement responsible AI engineering practices, including evaluation frameworks, ground truth governance, deployment standards, drift detection, and human accountability.

• Guide decisions on technical build/buy/modernize strategies.

• Collaborate with Product teams to address customer needs, outcomes, adoption, service boundaries, self-service capabilities, and delivery.

• Work together with Title Operations, Data Science, and engineering leaders.

• Build inclusive, psychologically safe, and distributed engineering teams.

• Lead through engineering managers and cultivate their development into leaders.

• Oversee hiring, performance, career development, succession, and organizational health.

• Manage the domain roadmap, investment tradeoffs, capacity planning, intake, commitments, dependencies, and delivery risks.

• Lead modernization efforts while safeguarding downstream consumers and ensuring business continuity.

• Establish operational standards for observability, service levels, incident response, on-call support, resilience, disaster recovery, release controls, and platform support.

• Enforce production-readiness guidelines for access control, lineage, auditability, governance, and data quality.

• Promote cost and performance discipline through metrics, postmortems, and automation.

⛳️ Requirements

• Proven experience in owning the strategy, architecture, and operational outcomes of a large-scale production data or AI platform that serves multiple teams, workloads, and users.

• Deep technical knowledge in distributed data processing, data lakes and lakehouses, cloud data warehouses, event-driven architectures, ingestion, orchestration, and production pipelines.

• Experience with document intelligence, unstructured data extraction, or ML-based data processing at a production scale.

• Strong technical understanding of AI/ML lifecycle management, encompassing model evaluation, ground truth governance, production deployment, versioning, and drift detection.

• Solid grasp of cloud networking, identity and access management, storage, compute, resilience, and cloud-native services.

• Technical expertise in Infrastructure as Code and CI/CD practices.

• Experience in modernizing complex data environments and migrating business-critical workloads without disrupting end-users.

• Strong architectural and vendor judgment, including evidence-based build/buy decision-making.

• Experience in shaping operating models and leading distributed teams through both technical and people leaders in a matrixed organization.

• Proven track record of establishing technical strategy, guiding investment decisions, and delivering measurable business outcomes alongside Product and business leaders.

• Practical skills in operational excellence, governance, security, data quality, cost management, and performance at scale.

• Outstanding executive communication and change leadership abilities.

• Inclusive leadership with a history of developing engineering managers and building a strong leadership pipeline.

• Ideally, familiar with Databricks, Snowflake, Unity Catalog, MLflow, event-driven architectures, document intelligence, OCR, extraction models, schema design, open table formats, data catalogs, lineage, semantic modeling, data contracts, AI platforms, and regulated industries.

🏝️ Benefits

• Medical insurance

• Dental insurance

• Vision insurance

• 401k

• PTO/paid sick leave

• Employee stock purchase plan

• Inclusive workplace and equal opportunity employment