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The Risk Onboarding team is working on efficiently reviewing customers’ applications without compromising on quality. We are the front line of defense for preventing money laundering and financial crimes, building systems to verify that someone is who they say they are and that we are allowed to do business with them. At Mercury, we are committed to crafting an exceptional banking* experience for startups. Our team is passionately focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators.
Job Responsibility:
Partner with data science & engineering teams to design and deploy ML & Gen AI microservices, primarily focusing on automating reviews
Work with a full-stack engineering team to embed these services into the overall review experience, including human in the loop, escalations, and feeding human decisions back into the service
Implement testing, observability, alerting, and disaster recovery for all services
Implement tracing, performance, and regression testing
Feel a strong sense of product ownership and actively seek responsibility – we often self-organize on small/medium projects, and we want someone who’s excited to help shape and build Mercury’s future
Requirements:
7+ years of experience in roles like machine learning engineering, data engineering, backend software engineering, and/or devops
Expertise with: A full modern data stack: Snowflake, dbt, Fivetran, Airbyte, Dagster, Airflow
SQL, dbt, Python
OLAP / OLTP data modelling and architecture
Key-value stores: Redis, dynamoDB, or equivalent
Streaming / real-time data pipelines: Kinesis, Kafka, Redpanda
API frameworks: FastAPI, Flask, etc.
Production ML Service experience
Working across full-stack development environment, with experience transferable to Haskell, React, and TypeScript
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