Descripción de la vacante
Sardine
€85,000 - €160,000 a year
Spain
Machine Learning Engineer en United Kingdom / Europe, España is listed on Jobeax. Browse 80,000+ vacancies available.
Domain knowledge in fraud, risk, or cybersecurity.
Background in Software Engineering
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.
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 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 https://jobeax.com/link/q5UJKXhptAfXLJ6b rely on Sardine to secure and grow trust in their products.
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.
Remote - UK, Germany, Netherlands, Ireland, Spain, Poland, Bulgaria or Lithuania
From Home / Beach / Mountain / Cafe / Anywhere!
We are a remote-first company with a globally distributed team. You can find your productive zone and work from there.
As a Machine Learning Engineer at Sardine, you'll own the systems that make real-time fraud detection possible. Our data science team builds custom models for our clients, you build and run the platform they deploy onto, and the low-latency serving path those models score on.
Sardine scores millions of sessions in real time from hundreds of device and behavioural signals, inside a sub-250ms budget. That constraint shapes everything: how features are computed and served, how models are deployed and rolled back, how quickly you know when something has degraded. You'll be the person who figures out why a model broke.
Build and own the model serving infrastructure, real-time inference, feature retrieval, and the latency budget that governs both
Build the deployment path our data scientists use to ship models themselves, including bring-your-own-model support for clients hosting their own
Own models in production: monitoring, drift detection, retraining, incident response, and the on-call rotation
Build and optimise the pipelines that turn raw device and behavioural signals into production-ready features
Work across Python and our Go backend to keep inference fast inside the request path
Build models yourself where it makes sense, roughly 20% of the role, and more if you want it
Champion testing, observability, security and compliance in a regulated environment
Experience building, not just using, model serving infrastructure.
Production ownership of ML systems: you've been paged when something broke, you found out why, and you changed something so it didn't happen again.
Strong Python, and solid software engineering fundamentals, testing, code review, CI/CD, the discipline that makes a platform other people can rely on.
Comfort with Kubernetes, containers and a major cloud (we're mostly GCP), plus infrastructure-as-code.
Enough understanding of models to debug them. You don't need to have trained one recently, but when precision drops you should know the difference between a data problem, a feature pipeline problem, and a model problem
Experience building tooling other engineers or data scientists actually use, and the judgement to know what should be self-serve and what shouldn't.
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