• Lead the architecture and end-to-end execution of the ML lifecycle - data strategy, model development, deployment, and continuous operation - primarily for computer vision and LLM/agentic systems • Own the MLOps foundation: training and deployment pipelines, model serving, CI/CD for models, and reproducible experimentation • Set and enforce standards for model accuracy and quality (evaluation frameworks, offline and online metrics, regression testing of models, and A/B testing) and hold the team to defined targets • Ensure production AI systems are scalable and highly available: define service-level objectives for latency, throughput, and uptime, and establish monitoring, drift detection, alerting, and rollback practices • Plan and manage team workload: delegate tasks, set daily, weekly, and monthly goals, and track progress against them • Partner with product, data engineering, DevOps/infrastructure, and clinical stakeholders to align priorities and drive projects forward

• The vision, architecture, and roadmap for ML/AI at Solidgate — a true greenfield direction, not a maintenance handover • Building and leading the team, combining new hires with engineers who already carry a deep payments context • A real-time ML decisioning system with direct revenue impact • A recommendation engine and other ML-driven features that lift merchant conversion and revenue • Integrations of cutting-edge AI technologies to drive innovation • Partnership with Product, Engineering, and the Data Platform team (who handle ML Ops) to solve hard modelling problems and build orchestrated training and analytics pipelines • Clear communication of complex results to audiences from engineers to executives

• Design and maintain scalable data pipelines and ETL/ELT workflows • Build and operate infrastructure for training, deploying, and serving ML models in production • Develop feature stores, vector databases, and other AI-enabling data infrastructure • Ensure reliability, low latency, and high availability of data systems • Partner closely with Research and Data Science to move findings into production • Implement monitoring and observability for data and model health • Contribute to infrastructure as code practices and documentation on cloud platforms

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.