Applied AI Research Scientist
Conduct applied research on foundation models for fraud detection using large-scale behavioral and financial-risk data. The role spans experimentation, evaluation, production deployment, and cross-functional work on model governance, requiring 4+ years of applied ML experience and strong Python and SQL skills.
About the job
Responsibilities
- Identify and scope opportunities, design rigorous experiments, and execute the roadmap for foundation-model research and development.
- Own evaluation standards for foundation-model performance, including offline benchmarks, time- and entity-aware holdouts, calibration, drift and degradation monitoring, and comparisons with strong classical baselines.
- Take models from data preparation and tokenization through pretraining, fine-tuning, distillation, quantization, and deployment in real-time inference paths with tight latency budgets.
- Partner with Engineering on training infrastructure, GPU efficiency, feature and embedding stores, and production-scale serving.
- Work with client-facing teams and customers to translate model capabilities and limitations into actionable risk-team decisions.
- Partner with Legal, Compliance, and customer model-risk teams on explainability, documentation, and governance for regulated customers.
Requirements
- 4+ years of experience in applied machine learning, quantitative modeling, or ML engineering.
- Experience pretraining or substantially adapting at least one foundation model and deploying it to real traffic.
- Hands-on self-supervised pretraining, fine-tuning, and model adaptation experience.
- Production experience with model serving, versioning, monitoring, and rollback.
- Ability to independently drive ambiguous applied-research projects and communicate clearly across data science, engineering, product, marketing, and external partner teams.
- Strong Python and SQL skills, with experience preparing very large datasets.
Nice-to-haves
- Experience in fraud, AML, payments, credit, or adversarial machine learning.
- Experience building and evaluating LLM-based agents in production.
- Publications, released models, or open-source contributions in representation learning or sequence modeling.
- Experience with model-risk management and documentation in a regulated financial environment.
Benefits
- Compensation in cash and equity.
- Early exercise for all options, including pre-vested options.
- Flexible paid time off and year-end break.
- Health, dental, and vision coverage for employees and dependents in the US and Canada.
- 401(k)/RRSP matching, MacBook Pro, home-office setup stipend, meal stipend, social-meetup stipend, health and wellness stipend, and annual learning stipend.
Skills
Deep Learning, Foundation Models, Machine Learning, Self-Supervised Learning, Fine-Tuning, Model Serving, Model Monitoring, Python, SQL, Tokenization, Model Distillation, Quantization, Gpu Computing, Sequence Modeling, Llm Agents
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