Remote Real-Time Fraud ML Engineer

sardine; United States; Remote; CAD 110,000 - 150,000; Full time

Benefits offered by this job

Generous compensation

Remote-first culture

Flexible paid time off

Health insurance

401k/RRSP matching

MacBook Pro

Home office stipend

Meal stipend

Social stipend

Learning stipend

Job summary

Sardine is hiring a Machine Learning Engineer to own real-time fraud detection systems. You will build and operate the model serving platform, orchestrate deployments, and ensure low-latency inference across signals from devices and behaviors.

You will work with Python and Go, handle drift detection, monitoring, and incident response, and contribute to self-serve tooling for engineers and data scientists.

Qualifications

• Experience building, not just using, model serving infrastructure.
• Production ownership of ML systems: paged when something broke and resolved root cause.
• Strong Python and software engineering fundamentals with CI/CD discipline.
• Comfort with Kubernetes, containers and a major cloud (GCP) and IaC.
• Understanding of models to debug data, features, or model issues.

Responsibilities

• Build and own the model serving infrastructure, real-time inference, feature retrieval, and the latency budget.
• Build the deployment path our data scientists use to ship models, including bring-your-own-model support.
• Own models in production: monitoring, drift detection, retraining, incident response, on-call rotation.
• Build and optimise pipelines turning signals into production-ready features.
• Work across Python and Go to keep inference fast in the request path.
• Develop models yourself where it makes sense, ~20% of the role, more if desired.

Skills

Python

Go

CI/CD

Kubernetes

Docker

Infra as code

Real-time inference

Model serving

Tools

Kubernetes

Docker

GCP

Job description

Sardine is hiring a Machine Learning Engineer to own real-time fraud detection systems. You will build and operate the model serving platform, orchestrate deployments, and ensure low-latency inference across signals from devices and behaviors.

You will work with Python and Go, handle drift detection, monitoring, and incident response, and contribute to self-serve tooling for engineers and data scientists.