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Director, Machine Learning
Envoy Global. Build and scale the ML engineering organization through hiring, pod structuring, and establishing a tech-lead layer .
About the role
Key responsibilities & impact- Build and scale the ML engineering organization through hiring, pod structuring, and establishing a tech-lead layer
- Mature the organization from ad-hoc experimentation to production-grade delivery through roadmap governance, automated testing, on-call ownership, and clear escalation/triage paths
- Manage, mentor, and grow senior ML engineers and data scientists
- Represent the ML organization to executive and cross-functional stakeholders
- Own technical strategy for document AI, extraction, and agentic systems applied to immigration case documents
- Lead build-vs-buy evaluations for ML capabilities and vendor tools, balancing cost, accuracy, latency, and compliance
- Design and own retrieval and context-optimization strategies including RAG, page/section narrowing, and agentic cross-validation
- Define and own ML systems architecture, including model serving, evaluation pipelines, and feature/data infrastructure
- Deliver measurable business outcomes through cost savings, document/extraction pipeline throughput, and accuracy/quality improvements
- Establish LLM evaluation frameworks and quality bars before production release
- 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 ML organization health, delivery, and cost/quality metrics to engineering and executive leadership
Requirements
What you’ll need- 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 demonstrating depth beyond applied delivery, such as issued patents, peer-reviewed publications, conference talks, or equivalent recognized contributions to the ML/AI field
- 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, including RAG, context optimization, evaluation, NER/document extraction, and traditional ML such as search/ranking and classification
- Experience making and defending build-vs-buy decisions for ML capabilities
- Experience 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 and ability 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