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 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. - We're a remote-first team spread across time zones, so no office to report to - work from wherever helps you do your best work. Just a couple of things to keep in mind: pay is based on where you're located, and you'll need to keep a home base in the country you're hired in. So while we love the "coffee shop today, mountains tomorrow" life, this isn't a passport-optional, work-from-anywhere-on-Earth kind of remote - think flexible within your country, not borderless. Location: - Remote -United States or Canada 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. To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice sardine.ai.

Requirements

Must-haves

Remote -United States or Canada
What you'll be doing
Identify and scope opportunities, design rigorous experiments, and execute on the roadmap for foundation model research and development.
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
Generous compensation in cash and equity
Early exercise for all options, including pre-vested
Work from anywhere: Remote-first Culture
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

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonsqlllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401k

Paid Time Off

Wellness