Director, Machine Learning

Accountabilities:: Build and scale the ML engineering organization, including hiring, team structure, technical leadership, and development of a strong ownership model. Establish a technical-lead layer that enables teams to make effective day-to-day technical decisions as the organization grows. Mature ML development from experimentation into production-grade delivery through roadmap governance, automated testing, on-call ownership, and clear escalation and triage processes. Lead, mentor, and develop senior ML engineers and data scientists while representing the ML organization with executive and cross-functional stakeholders. Define the technical strategy for document AI, information extraction, NLP, retrieval, and agentic systems supporting complex case documents and data. Lead structured build-versus-buy evaluations for ML capabilities, balancing accuracy, cost, latency, compliance, scalability, and operational requirements. Design retrieval and context-optimization strategies, including RAG, page and section narrowing, and agentic cross-validation, to manage LLM inference costs while maintaining accuracy. Own ML systems architecture covering model serving, evaluation pipelines, feature and data infrastructure, and related platform capabilities in partnership with engineering and product architects. Establish measurable quality standards and LLM evaluation frameworks before models are deployed to production. Drive continuous optimization of model and pipeline performance, cost, quality, and throughput. Own measurable business outcomes, including automation-driven cost savings, document processing throughput, and accuracy improvements for case-critical data. Partner with Product, Legal Operations, and Case Management leadership to translate workflow requirements into ML-powered product capabilities. Report on ML organization health, delivery progress, operational performance, and cost and quality metrics to engineering and executive leadership. Requirements: 8+ years of experience in applied machine learning or AI, including several years leading or managing ML/AI engineering teams. Experience in document understanding, NLP, search, information extraction, or related areas. Demonstrated depth and recognized contributions to the ML/AI field through patents, peer-reviewed publications, conference presentations, or equivalent achievements. Proven track record of scaling an ML/AI organization and delivering production LLM, NLP, or document-extraction systems at significant volume. Experience owning measurable cost, quality, accuracy, and performance outcomes for production ML systems. Strong hands-on expertise with LLMs and agentic systems, including RAG, context optimization, evaluation frameworks, and production deployment. Deep knowledge of NER, document extraction, search and ranking, classification, and traditional machine learning techniques. Ability to work directly with technical teams on complex ML problems while providing effective organizational and strategic leadership. Experience making and defending build-versus-buy decisions for ML capabilities and partnering with architects on ML platform and infrastructure strategy. Experience in healthcare, legal, financial services, or another regulated environment involving sensitive documents is strongly preferred. Excellent executive communication skills, with the ability to translate technical trade-offs, risks, and opportunities into clear business terms. Strong organizational, strategic thinking, mentoring, and cross-functional collaboration skills. M.S. or Ph.D. in Computer Science, Machine Learning, or a related technical field is preferred. Ability to work remotely from within the United States. Benefits: Annual salary range of $200,000–$245,000. 100% remote work-from-home position within the United States. Opportunity to build and lead an AI/ML organization with significant ownership of technical strategy and production outcomes. Opportunity to work on document AI, LLMs, agentic automation, NLP, and other advanced machine learning applications. Cross-functional collaboration with Product, Engineering, Legal Operations, and Case Management teams. Leadership opportunity combining organizational development, technical strategy, and hands-on ML expertise.