# Data Engineer - Onboarding at Sardine

- Company: Sardine
- What the company does: The top agentic risk platform used by leading banks and merchants worldwide to stop fraud in real-time, prevent AI-driven attacks, automate fraud & AML operations. Backed by a16z and GV.
- Company website: https://www.sardine.ai
- Type: Startups (AI role)
- Level: Mid level
- Location: North America
- Work setup: Remote
- Pay: $150K to $205K base salary per year (USD)
- Posted: 2026-07-30
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/sardine/527bac62-7445-4942-b2df-575fcb93e182
- Page: https://www.1752.vc/careers/jobs/sardine-data-engineer-onboarding/

## About the role

We are looking for a Senior Data/ML Engineer to own the data and machine learning foundation that Sardine's compliance decisions run on. Every onboarding decision we make — a payment approved, an account blocked, a KYC case escalated — is the output of a pipeline someone built. This role owns those pipelines end to end: how data arrives, how it becomes a feature, how that feature becomes a model, and how that model stays correct in production.

## What they're looking for

- 8+ years building production data and ML systems, with real ownership of both the pipeline side and the model side. You have shipped models that made consequential automated decisions, not just dashboards
- Hands-on experience with a modern cloud data stack: GCP strongly preferred (BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, Composer, Vertex AI) or the AWS equivalents, plus Docker, Kubernetes, Terraform, and CI/CD
- Practical ML engineering depth: feature stores and feature pipelines, training/serving skew, gradient-boosted tree models, class imbalance and rare-event modeling, threshold and cost-sensitive tuning, model monitoring and drift detection, and explainability
- Experience with high-volume, low-latency serving where a feature fetch has a few hundred milliseconds and there is no retry budget
- Comfort with data governance in a regulated environment: PII, encryption, access control, regional data residency, auditability
- Strong written communication. You can explain a modeling tradeoff to a fraud analyst and a pipeline design to a backend engineer, and you write things down

Tags: Engineering