Applied AI Research Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied AI Research Scientist based in Canada.

As an Applied AI Research Scientist, you’ll apply deep learning and foundation model expertise to some of the most challenging problems in fraud detection and financial risk. You’ll work with rich, large-scale behavioral and sequential datasets spanning device intelligence, biometrics, telemetry, payments, and consortium signals. The role combines cutting-edge applied research with hands-on production engineering, taking models from experimentation through real-time deployment. You’ll help define and execute the roadmap for next-generation fraud foundation models while establishing rigorous standards for model evaluation and performance. Collaboration will span data science, engineering, product, compliance, legal, and customer-facing teams. You’ll also work directly with financial institutions and fintech organizations to translate model capabilities into practical risk decisions. This is a remote-first opportunity for an independent, highly motivated scientist who wants their research to create measurable real-world impact.

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.