Director, Machine Learning

Envoy Global is a proven innovator in the global immigration space. Our mission combines our industry-leading tech platform with holistic service to streamline, simplify, and expedite the immigration process for employers and individuals.

Envoy Global is looking for a Director, Machine Learning to build and lead the AI/ML organization behind our immigration case management platform — including document understanding, extraction, and agentic automation across visa petitions, supporting evidence, and case correspondence. You’ll grow and manage a team of ML engineers, set technical direction on build-vs-buy for AI/ML capabilities, and be accountable for the cost, quality, and throughput of every model in production. You bring not just delivery experience but recognized depth in the field — patents, publications, or equivalent proven credentials that show you can push the state of the art, not just apply it.

As our Director, Machine Learning, you will be required to:

Team & Organization Leadership

• Build and scale the ML engineering organization — hiring, structuring pods, and establishing a tech-lead layer so the team can own day-to-day technical decisions as it grows.
• Mature the org from ad-hoc experimentation to production-grade delivery through roadmap governance, automated testing, on-call ownership, and clear escalation/triage paths for model and pipeline issues.
• Manage, mentor, and grow senior ML engineers and data scientists, and represent the ML org to executive and cross-functional stakeholders.

ML Platform Strategy & Build-vs-Buy

• Own the technical strategy for document AI, extraction, and agentic systems applied to immigration case documents — petitions, supporting evidence, correspondence, and case data.
• Lead structured build-vs-buy evaluations for ML capabilities and vendor tools — in-house models and pipelines versus external vendors or managed services — balancing cost, accuracy, latency, and compliance.
• Design and own retrieval and context-optimization strategies (RAG, page/section narrowing, agentic cross-validation) that control LLM inference cost at scale without sacrificing accuracy.
• Define and own the ML systems architecture — model serving, evaluation pipelines, feature/data infrastructure — in partnership with platform and product architects.

Delivery & Operational Impact

• Be accountable for measurable business outcomes: cost savings from displacing manual review or external vendors, throughput scaling of document/extraction pipelines, and accuracy/quality gains on case-critical data.
• Establish LLM evaluation frameworks and quality bars before models ship to production, and drive continuous model and pipeline cost optimization.
• Partner with Product, Legal Operations, and Case Management leadership to translate immigration workflow requirements into ML-backed product capabilities.
• Report on ML org health, delivery, and cost/quality metrics to engineering and executive leadership.

To apply for this role, you should possess the following skills, experience, and qualifications:

• 8+ years in applied ML/AI, including several years leading or managing an ML/AI engineering team, ideally in document understanding, NLP, or search.
• Proven credentials that demonstrate depth beyond applied delivery — issued patents, peer-reviewed publications or conference talks, or equivalent recognized contributions to the ML/AI field.
• A track record scaling an ML/AI organization and shipping production LLM, NLP, or document-extraction systems at volume, with clear ownership of cost and quality outcomes.
• Hands-on depth in LLM and agentic systems (RAG, context optimization, evaluation), NER/document extraction, and traditional ML (search/ranking, classification) — comfortable going deep with the team, not just directing from above.
• Experience making and defending build-vs-buy calls for ML capabilities, and partnering with architects on platform-level ML infrastructure decisions.
• Experience in healthcare, legal, financial services, or other regulated/compliance-sensitive domains handling sensitive documents is a strong plus.
• Excellent executive communication skills; able to translate technical trade-offs into business terms for non-technical stakeholders.
• M.S. or Ph.D. in Computer Science, Machine Learning, or a related field preferred.