Role Description The Lead Evaluator, Early Learning will provide high quality personalized and customized service to early learning institutions (birth through kindergarten) in the Cognia network, particularly in relation to accreditation evaluation. Lead Evaluators serve as an extension of the local Cognia office to deliver a variety of services related to accreditation. - Conducts various types of Cognia reviews, including Accreditation Engagement, Monitoring, and Candidacy Reviews, ensuring alignment with Cognia’s standards and expectations. - Successfully completes a comprehensive training program consisting of multiple courses that equip evaluators to conduct all types of Cognia reviews with consistency and quality. - Participates in ongoing professional development and in-service training to continuously enhance evaluation expertise and remain current with Cognia protocols. - Delivers technical assistance and accreditation-related training to institutions, supporting them throughout the accreditation lifecycle. - Supports institutions during their six-year accreditation cycles by offering expert guidance and resources tailored to their specific needs. - Provides technical support for Cognia’s accreditation tools, including both online and offline platforms. - Facilitates high-quality training sessions to prepare institutions for upcoming reviews, ensuring a thorough understanding of accreditation expectations and processes. - Offers post-review support to institutions, including interpretation of findings and strategic guidance for improvement planning to advance institutional goals. - Serves as a representative of Cognia in professional settings, promoting the organization’s mission and services within the broader education community. - Delivers presentations and informational sessions on Cognia accreditation and improvement services at institutional meetings, conferences, and other relevant forums. - Participates in school and system meetings on behalf of Cognia to provide feedback, present recognition, and support institutional engagement. - Collaborates with regional and state offices to execute service plans and initiatives that enhance the accreditation process. - May review reports and/or serve in a mentoring or coaching role for other accreditation team members to support capacity building and quality assurance. - Assists Senior Director(s) with quarterly newsletter to institutions preparing for or going through an Accreditation Engagement Review. - Performs duties and fulfills responsibilities that may, from time to time, include related or unrelated tasks. Qualifications - BS in Early Childhood Education or equivalent required; M.Ed. Ed.D. or Ph.D. in educational leadership area preferred. - A minimum of five (5) years of Birth through Kindergarten teaching experience required. - Three (3) or more years in a leadership capacity in an early learning school district early learning program, early learning system of schools or early childhood education focused not for profit or state agency preferred. - Experience as an educational leader at the building or central office level desired. - Evaluators are expected to complete required initial and ongoing training on reviews and interpretations. - Evaluators are expected to maintain a high level of knowledge about the current operations of Cognia by participating in ongoing professional development related to Cognia’s work. - Certification for eleot™ and/or erel™ required. - Experience with Cognia tools and processes preferred. Requirements - Exceptional oral and written communication skills. - Demonstrated leadership and interpersonal skills. - Substantial experience in the field of education as a school or district leader. - Knowledge of Cognia processes and protocols. - Extensive travel (>40%). - Travel to All Staff Company Meeting required. Benefits - All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy-related conditions), sexual orientation, gender identity, marital status, national origin, age, physical or mental disability, citizenship, protected veteran status, genetic information or any other characteristics protected by local, state, or federal laws, rules, or regulations. - Cognia is an Equal Opportunity Employer.

• Deployment and integration of computer vision solutions for next-generation satellite imagery analytics. • Ensure high-quality and production-ready Python or Go code. • Manage cloud infrastructure and code deployment to AWS, Kubernetes, or other environments. • Take part in the entire project lifecycle from requirements development to deployment. • Serve as a lead on MLOps software development projects, data ETL projects, and software feature development. • Collaborate with management and technical team on technical roadmap and implementation strategy.

Role Description As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud, account takeover, identity abuse, merchant and marketplace risk, scams, and other adversarial activity across Block. The team optimizes for reliable decisions, safe deployment, and measurable customer outcomes — preserving access for good customers while reducing fraudulent, abusive, or unsafe activity. You should be comfortable owning production systems end to end: - Data contracts - Low-latency inference - Batch scoring - Feature quality - Online/offline consistency - Model deployment - Monitoring - Incident response - Rollback - Outcome feedback loops The work combines large-scale ML decisioning with AI-assisted operations: - Surfacing evidence - Simulating controls - Accelerating triage - Improving feedback loops while preserving human judgment in high-stakes decisions You will work closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to respond quickly to evolving abuse patterns without creating unnecessary friction or harm for legitimate customers. Qualifications - 12+ years building and operating production software and ML systems for business-critical products - Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning - Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response - Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions - Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems Requirements - Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review - Experience building fraud operations tooling for triage, case management, alert clustering, graph exploration, or policy simulation - Experience with regulated financial services, model governance, auditability, explainability, or decision logging Technologies We Use and Teach - Python, Java, Kotlin, SQL - TensorFlow, PyTorch, XGBoost/LightGBM, embeddings, deep learning, and tree-based modeling ecosystems - Kafka or other event-streaming systems, batch data pipelines, feature stores, workflow orchestration, and model-serving systems - Cloud infrastructure, Kubernetes, data warehouses/lakehouses, monitoring, observability, coding agents, evaluation harnesses, and agent-assisted operations tooling Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning

• Build and deploy models to detect and flag suspicious behavior (classification, anomaly detection, clustering, ranking, and other approaches as appropriate) • Own ML pipelines end-to-end (feature generation, training, evaluation, batch/streaming inference, backfills, and versioning) • Design evaluation + monitoring: ground truth strategies with Threat Intel, offline metrics, drift monitoring, and alerting • Collaborate with product & engineering to integrate intelligence into user-facing workflows (e.g., review queues, and decision-support tooling) • Improve data quality and accessibility by working with data engineering patterns (schemas, lineage, reproducibility, and access controls) • Contribute to operational rigor: runbooks, incident response, and safe rollout practices for systems that impact customer trust