Machine Learning Engineer

Machine Learning Engineer

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Who We Are

We are a leader in fraud prevention and AML compliance.

Our platform uses device intelligence, behavior biometrics, machine learning, and AI to stop fraud before it happens.

Today, over 300 banks, retailers, and fintechs worldwide use Sardine to stop identity fraud, payment fraud, account takeovers, and social engineering scams.

We have raised $145M from world-class investors, including Andreessen Horowitz, Activant, Visa, Experian, FIS, and Google Ventures.

Our Culture

We have hubs in the Bay Area, NYC, Austin, and Toronto.

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 - Brazil

To be considered for this position, you must reside in one of the following cities :

São Paulo : São Paulo, Campinas, São José dos Campos

Rio de Janeiro : Rio de Janeiro

Minas Gerais : Belo Horizonte

Paraná : Curitiba

Santa Catarina : Florianópolis

About The Role

As a Machine Learning Engineer, you'll do more than build models - you'll design the systems that make fraud detection possible.

You'll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale.

This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.

What You'll Do

Build and optimize data pipelines and backend services to process device and behavioral data in real time.

Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.

Turn raw data into production-ready features that feed our fraud detection systems.

Collaborate with platform and backend engineers to integrate models seamlessly.

Maintain high standards of security, privacy, and compliance.

Champion best practices in testing, documentation, and observability.

What You Bring

5+ years in software engineering, with strong backend experience (Go or Python).

Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).

Strong SQL skills and familiarity with relational and non-relational databases.

Experience with end-to-end ML systems : feature pipelines, model deployment, monitoring, and iteration.

Excellent communication skills in English, both written and verbal.

Bonus Points

Domain knowledge in fraud, risk, or cybersecurity.

Familiarity with CI / CD, Docker, Kubernetes and the modern devops framework.

Understanding of modern browser APIs and high-entropy data collection techniques.

Familiarity with leveraging frontier LLMs for automation.

Compensation

Base pay range of BRL $330,000 - BRL $440,000 + Series C equity with tremendous upside potential + Attractive benefits.

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, Year-end break, Self-care days off

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

Unlimited access to an expert financial advisory

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.

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