Applied AI Research Scientist

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

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: device intelligence, behavior biometrics, session telemetry, payment events, and consortium signals that we leverage to fight fraud across hundreds of fintechs and banks. We are looking for an applied research scientist that brings their expertise in deep learning and foundation models to take this to the next level.

We are looking for an experienced 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 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.

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 substantially adapted and put in front of real traffic
• 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

Nice to haves

• Background 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 we offer:

• Generous compensation in cash and equity
• Early exercise for all options, including pre-vested
• Work from anywhere: Remote-first Culture
• Flexible paid time off and Year-end break
• Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific
• 4% matching in 401k / RRSP - US and Canada specific
• MacBook Pro delivered to your door
• One-time stipend to set up a home office — desk, chair, screen, etc.
• Monthly meal stipend
• Monthly social meet-up stipend
• Annual health and wellness stipend
• Annual Learning stipend

Join a fast-growing company with world-class professionals from around the world. If you are seeking a meaningful career, you found the right place, and we would love to hear from you.

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