Applied AI Research Scientist
AI summary of the role
Sardine is hiring an Applied AI Research Scientist to lead the development of next-generation fraud foundation models using rich behavioral data (device intelligence, biometrics, payment events).
What you’ll do
- Scope and execute the roadmap for foundation model research and development
- Own evaluation bar: offline benchmarks, time/entity-aware holdouts, calibration, drift monitoring, head-to-head vs classical baselines
- Take models from data prep and tokenization through pretraining, fine-tuning, distillation, quantization, and real-time deployment
- Partner with Engineering on training infrastructure, GPU efficiency, feature/embedding stores, and serving at scale
What you’ll bring
- 4+ years in applied ML, quantitative modeling, or ML engineering with at least one foundation model pretrained or substantially adapted and put in front of real traffic
- Hands-on self-supervised pretraining experience plus practical fine-tuning and adaptation
- Production experience with model serving, versioning, monitoring, and rollback
- Strong Python and SQL, comfort with very large datasets
Technologies
deep learning · foundation models · self-supervised pretraining · fine-tuning · distillation · quantization · model serving · Python · SQL · LLM agents
About Sardine
AI-powered unified fraud prevention and AML compliance platform protecting financial institutions, fintechs, and merchants from identity fraud, payment fraud, and money laundering.
Series C · 200–500 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
foundation model expertise with rich non-text sequential data to come up with practical, state-of-the-art fraud detection solutions. You will have an opportunity to scope and drive the next generation of fraud foundation models at Sardine, and drive industry-wide adoption. What you'll be doing Identify and scope opportunities, design rigorous experiments, and execute on the roadmap for foundation model research and development. Own the evaluation bar for foundation model performance: offline benchmarks, time- and entity-aware holdouts, calibration, drift and degradation monitoring, and honest head-to-head comparisons against strong classical baselines. Take models the full distance from data prep and tokenization through pretraining, fine-tuning, distillation, quantization, and deployment behind a real-time inference path with tight latency budgets. Partner with Engineering on
More from the job description
Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. Location: Remote -United States or Canada From Home / Beach / Mountain / Cafe / Anywhere! We are a remote-first company with a globally distributed team. So you can find your productive zone and work from there. About the role Sardine sits on one of the richest behavioral datasets in fraud and risk: devic [... source excerpt omitted ...] erienced ML applied scientist that can combine foundation model expertise with rich non-text sequential data to come up with practical, state-of-the-art fraud detection solutions. You will have an opportunity to scope and drive the next generation of fraud foundation models at Sardine, and drive industry-wide adoption. What you'll be doing Identify and scope opportunities, design rigorous experiments, and execute on the roadmap for foundation model research and development. Own the evaluation bar for foundation model performance: offline benchmarks, time- and entity-aware holdouts, calibration, drift and degradation monitoring, and honest head-to-head comparisons against stron [... source excerpt omitted ...] nt behind a real-time inference path with tight latency budgets. Partner with Engineering on training infrastructure, GPU efficiency, feature and embedding stores, and serving at production scale Work directly with client-facing teams and customers to turn model capabilities and limits into decisions their risk teams can act on. Partner with Legal, Compliance, and customer model risk teams to build the explainability, documentation, and governance our bank and fintech customers need to satisfy their own regulators. What you'll need 4+ years in applied machine learning, quantitative modeling, or ML engineering including at least one foundation model you pre trained or substanti
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