Mercury
Overview
San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States At Mercury, the Risk Onboarding team reviews customers’ applications to prevent money laundering and financial crimes, while building systems to verify identities and ensure we are allowed to do business with them. We are committed to crafting an exceptional banking experience for startups and ensuring our products create a safe environment for customers, administrators, and regulators. Mercury is a financial technology company, not a bank. Banking services are provided through Choice Financial Group, Column N.A., and Evolve Bank & Trust, Members FDIC. Responsibilities
Partner with data science and engineering teams to design and deploy ML and 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 Demonstrate strong product ownership and actively seek responsibility; contribute to Mercury’s future by helping shape and build the product Qualifications
7+ years of experience in roles such as machine learning engineering, data engineering, backend software engineering, and/or devops Expertise with: A modern data stack: Snowflake, dbt, Fivetran, Airbyte, Dagster, Airflow SQL, dbt, Python OLAP/OLTP data modeling and architecture Key-value stores: Redis, DynamoDB, or equivalent Experience across full-stack development with transferable skills to Haskell, React, and TypeScript Compensation and Benefits
The total rewards package includes base salary, equity, and benefits. Salary and equity ranges are competitive in the SaaS and fintech industries and are updated based on reliable compensation survey data. New hire offers consider experience, location, and internal pay equity relative to peers. US employees (any location): $200,700 - $250,900 Canadian employees (any location): CAD 189,700 - 237,100 Mercury values diversity and belonging and is an Equal Employment Opportunity employer. All individuals seeking employment are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We provide reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, please let your recruiter know once you are contacted about a role. We use Covey as part of our hiring and/or promotional process for jobs in NYC; certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submissions. We began using Covey Scout for inbound on January 22, 2024. See the independent bias audit report covering our use of Covey. #LI-RA1
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San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States At Mercury, the Risk Onboarding team reviews customers’ applications to prevent money laundering and financial crimes, while building systems to verify identities and ensure we are allowed to do business with them. We are committed to crafting an exceptional banking experience for startups and ensuring our products create a safe environment for customers, administrators, and regulators. Mercury is a financial technology company, not a bank. Banking services are provided through Choice Financial Group, Column N.A., and Evolve Bank & Trust, Members FDIC. Responsibilities
Partner with data science and engineering teams to design and deploy ML and 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 Demonstrate strong product ownership and actively seek responsibility; contribute to Mercury’s future by helping shape and build the product Qualifications
7+ years of experience in roles such as machine learning engineering, data engineering, backend software engineering, and/or devops Expertise with: A modern data stack: Snowflake, dbt, Fivetran, Airbyte, Dagster, Airflow SQL, dbt, Python OLAP/OLTP data modeling and architecture Key-value stores: Redis, DynamoDB, or equivalent Experience across full-stack development with transferable skills to Haskell, React, and TypeScript Compensation and Benefits
The total rewards package includes base salary, equity, and benefits. Salary and equity ranges are competitive in the SaaS and fintech industries and are updated based on reliable compensation survey data. New hire offers consider experience, location, and internal pay equity relative to peers. US employees (any location): $200,700 - $250,900 Canadian employees (any location): CAD 189,700 - 237,100 Mercury values diversity and belonging and is an Equal Employment Opportunity employer. All individuals seeking employment are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We provide reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, please let your recruiter know once you are contacted about a role. We use Covey as part of our hiring and/or promotional process for jobs in NYC; certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submissions. We began using Covey Scout for inbound on January 22, 2024. See the independent bias audit report covering our use of Covey. #LI-RA1
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