Role Description We are looking for a Senior Applied Machine Learning Scientist with a mission of building and owning the ML systems that power ThoughtExchange’s AI capabilities. In this role, you’ll take end-to-end ownership of complex machine learning projects from research and experimentation through to production that directly shape how our platform delivers insights to leaders across North America. As the foundational ML scientist on the team, your technical decisions will have an outsized impact on our product and the communities we serve. If you thrive in exploratory problem-solving, care deeply about technical quality, and want to see your work make a real difference, we’d love to hear from you. What You’ll Do - Leads and is accountable for the full machine learning lifecycle from research and experimentation through to production, applying scientific methodology and sound technical trade-offs to deliver robust, scalable solutions. - Build and own the AI quality platform, including evaluation frameworks, monitoring, and guardrails, enabling engineers across the organization to safely iterate on prompts and models. - Research emerging ML technologies and methodologies, and drive adoption decisions for the team’s tools and processes. - Leads open-ended analytical investigations, translating ambiguous business questions into structured approaches and actionable findings. - Leads the design of scalable, maintainable ML solutions, proactively managing technical debt and anticipating future needs. - Write clean, testable, and well-documented code for customer-facing features, data pipelines, and experimentation, and debug complex issues across product areas to resolve root causes. - Collaborate with Engineering, Product, and Design teams to elevate the team’s technical practices and influence priorities. Qualifications - 5+ years of professional experience in machine learning, data science, or software engineering with applied ML responsibility. - Strong programming skills in Python, with experience using common machine learning libraries and frameworks. - Deep understanding of machine learning fundamentals, statistical modeling, evaluation techniques, and system design principles. - Hands-on experience deploying and supporting machine learning models in production environments. - Experience working with relational databases (e.g., PostgreSQL) and large datasets. - Experience building or working with LLM evaluation and observability tooling (e.g., evals frameworks, prompt versioning, model comparison pipelines). - Comfortable operating in both exploratory, ambiguous analytical contexts and structured production delivery. - Strong collaboration skills with the ability to explain complex technical concepts to non-technical colleagues. Nice to Have - Master’s or PhD in Machine Learning, Data Science, or a related field. - Familiarity with cloud-based systems and services, preferably AWS. - Familiarity with software engineering best practices including version control, testing, and CI/CD. Salary Range The hiring range for this role is $141,848–157,609 CAD. Your specific compensation within this range is determined based on your job-related skills, knowledge, experience, and our internal equity assessment. Benefits - From day one, you’ll receive a benefits package focused on health & wellness that includes a generous time off policy, flexible extended benefits plan options, and company-wide wellness days off scheduled throughout the year. - Our benefits package also includes maternity & parental leave top-up programs, as well as access to Maple & Inklbot, which support our employees' primary care, mental health, and wellness needs. - We’ve been remote-first for over ten years. We’re contribution-focused and operate on mutual trust because we need you to feel empowered to be your best self. - We walk the walk when it comes to our product, ensuring that no critical decisions are made without including our employees' perspectives. - We want you to do your best work, and part of that is being happy with your compensation. We pay fairly, considering the complexities of market rates, experience, location, and demand. - In addition to competitive pay and benefits, employees receive share options when joining the company. - We host regular learning sessions. You also have access to an annual Professional Development stipend & Company Coach to ensure you can grow in your role & advance your career.

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