Applied AI Research Scientist

Accountabilities

• Identify high-value opportunities for foundation model research and development, scope initiatives, design rigorous experiments, and drive execution against the research roadmap.
• Develop next-generation fraud detection solutions by applying foundation models, deep learning, and representation learning to large-scale non-text sequential data.
• Establish and maintain a high evaluation standard through offline benchmarks, time- and entity-aware holdouts, calibration analysis, drift monitoring, degradation tracking, and comparisons against strong classical baselines.
• Take models through the complete machine learning lifecycle, including data preparation, tokenization, pretraining, fine-tuning, distillation, quantization, deployment, and production optimization.
• Partner with engineering teams on training infrastructure, GPU utilization and efficiency, feature and embedding stores, model serving, and scalable real-time inference.
• Ensure models meet demanding production requirements, including tight latency constraints, reliable serving, version control, monitoring, and rollback capabilities.
• Collaborate with client-facing teams and customers to communicate model capabilities, limitations, and performance in ways that enable actionable risk-management decisions.
• Work with legal, compliance, and customer model-risk teams to develop appropriate explainability, documentation, governance, and validation practices for regulated financial environments.
• Independently manage ambiguous applied research projects while maintaining clear communication and alignment with internal and external stakeholders.
• Contribute to the advancement and adoption of practical, state-of-the-art AI approaches within fraud detection and financial risk.

Requirements

• 4+ years of experience in applied machine learning, quantitative modeling, ML engineering, or a closely related field.
• Hands-on experience pretraining or substantially adapting at least one foundation model and deploying it in a production environment with real-world traffic.
• Strong practical experience with self-supervised pretraining, fine-tuning, and model adaptation techniques.
• Production experience with model serving, versioning, monitoring, and rollback processes.
• Strong Python and SQL skills, with demonstrated ability to prepare, process, and analyze very large datasets.
• Ability to independently scope and execute ambiguous research and development projects from experimentation through production.
• Excellent communication and collaboration skills, with the ability to work effectively across data science, engineering, product, marketing, compliance, legal, and external partner teams.
• Strong research mindset combined with a practical, outcome-oriented approach to building production-ready machine learning systems.
• Experience in fraud, AML, payments, credit, or adversarial machine learning is a strong asset.
• Experience building and evaluating LLM-based agents in production is a plus.
• Publications, released models, or open-source contributions involving representation learning or sequence modeling are advantageous.
• Experience with model risk management, regulatory documentation, and governance in financial services is a plus.

Benefits

• Generous compensation package combining cash and equity.
• Early exercise opportunities for all stock options, including pre-vested options.
• Remote-first culture with the flexibility to work from anywhere.
• Flexible paid time off and a year-end company break.
• Health, dental, and vision insurance for employees and eligible dependents in Canada.
• 4% RRSP matching for eligible Canadian employees.
• MacBook Pro provided for work.
• One-time home office setup stipend for equipment such as a desk, chair, and monitor.
• Monthly meal stipend.
• Monthly social meetup stipend.
• Annual health and wellness stipend.
• Annual learning and professional development stipend.
• Opportunity to work alongside experienced professionals on complex, high-impact AI and financial risk challenges.
• Globally distributed, collaborative environment with a strong emphasis on autonomy, ownership, and results rather than hours worked.