Remote Applied AI Research Scientist - Fraud & Risk

Sardine; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity

Remote-first culture

Health insurance

401k/RRSP matching

MacBook Pro provided

Home office stipend

Meal stipend

Learning stipend

Job summary

Sardine is seeking an experienced ML Applied Scientist to advance foundation models for real-time fraud detection across a rich risk dataset. You will scope opportunities, design rigorous experiments, and drive the model lifecycle from data prep to deployment behind a low-latency inference path.

You will collaborate with engineering, data science, and product teams, and engage with banks and fintech customers to meet regulatory and governance needs while delivering practical, scalable solutions.

Qualifications

• 4+ years in applied machine learning, quantitative modeling, or ML engineering, including at least one foundation model pretrained or substantially adapted.
• Hands-on self-supervised pre-training experience, plus practical fine-tuning and adaptation.
• Production experience with model serving, versioning, monitoring, and rollback.
• Ability to self-manage and drive ambiguous applied research projects with clear communication with partner teams across data science, engineering, product, marketing and external partners.
• Strong Python, strong SQL, and comfort preparing very large datasets.

Responsibilities

• 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 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.

Skills

Python

SQL

Self-management

Experiment design

Job description

Sardine is seeking an experienced ML Applied Scientist to advance foundation models for real-time fraud detection across a rich risk dataset. You will scope opportunities, design rigorous experiments, and drive the model lifecycle from data prep to deployment behind a low-latency inference path.

You will collaborate with engineering, data science, and product teams, and engage with banks and fintech customers to meet regulatory and governance needs while delivering practical, scalable solutions.