Applied AI Research Scientist

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Identify and assess opportunities, design thorough experiments, and implement the roadmap for foundation model research and development.

• Take responsibility for evaluating foundation model performance, which includes offline benchmarks, time- and entity-aware holdouts, calibration, drift and degradation monitoring, as well as comparisons with traditional baselines.

• Manage the transition of models from data preparation and tokenization through pretraining, fine-tuning, distillation, quantization, and deployment in real-time inference paths with strict latency requirements.

• Collaborate with Engineering to enhance training infrastructure, GPU efficiency, feature and embedding stores, and production-level serving.

• Engage with client-facing teams and customers to convert model capabilities and limitations into actionable insights for risk management teams.

• Work closely with Legal, Compliance, and customer model risk teams on issues of explainability, documentation, and governance for regulated banking and fintech clients.

• Define and lead the development of the next generation of fraud foundation models and promote industry-wide adoption.

⛳️ Requirements

• A minimum of 4 years of experience in applied machine learning, quantitative modeling, or ML engineering.

• At least one foundation model that has been pre-trained or significantly adapted and deployed in a real-world setting.

• Practical experience with self-supervised pre-training.

• Experience in fine-tuning and adapting models effectively.

• Production experience in model serving, versioning, monitoring, and rollback processes.

• Capability to self-manage and lead ambiguous applied research initiatives.

• Excellent communication skills with partner teams across data science, engineering, product, marketing, and external stakeholders.

• Proficiency in Python.

• Strong SQL skills.

• Comfort with handling very large datasets.

• A background in fraud, AML, payments, credit, or adversarial machine learning is advantageous.

• Experience in building and evaluating LLM-based agents in a production environment is a plus.

• Publications, released models, or open-source contributions in representation learning or sequence modeling are favorable.

• Familiarity with model risk management and documentation in a regulated financial setting is a plus.

🏝️ Benefits

• Competitive compensation in both cash and equity.

• Early exercise options available for all options, including those that are pre-vested.

• Remote-first culture allowing work from anywhere.

• Flexible paid time off and a year-end break.

• Health insurance, dental, and vision coverage for employees and their dependents - specific to the US and Canada.

• 4% matching in 401k / RRSP - specific to the US and Canada.

• MacBook Pro delivered directly to your home.

• One-time stipend for setting up a home office, including desk, chair, screen, etc.

• Monthly meal stipend.

• Monthly stipend for social meet-ups.

• Annual stipends for health and wellness.

• Annual learning stipend.