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.