Senior Machine Learning Engineer
Before 1965, it was extremely difficult and time-consuming to analyze complicated signals, like radio or images. You could solve it, but you had to throw a ton of compute at it. That all changed with the invention of the Fast Fourier transform, which could efficiently break that signal down into the frequencies that are a part of it. 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.
*Mercury is a financial technology company, not a bank. Banking services provided through Choice Financial Group, Column N.A., and Evolve Bank & Trust, Members FDIC.
As part of this role, you will:
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
The ideal candidate for the role has:
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
The total rewards package at Mercury includes base salary, equity, and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location): $200,700 - $250,900
Canadian employees (any location): CAD 189,700 - 237,100
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury 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 are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, 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 and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.
#LI-RA1
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Senior Machine Learning Engineer
Before 1965, it was extremely difficult and time-consuming to analyze complicated signals, like radio or images. You could solve it, but you had to throw a ton of compute at it. That all changed with the invention of the Fast Fourier transform, which could efficiently break that signal down into the frequencies that are a part of it. 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.
*Mercury is a financial technology company, not a bank. Banking services provided through Choice Financial Group, Column N.A., and Evolve Bank & Trust, Members FDIC.
As part of this role, you will:
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
The ideal candidate for the role has:
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
The total rewards package at Mercury includes base salary, equity, and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location): $200,700 - $250,900
Canadian employees (any location): CAD 189,700 - 237,100
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury 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 are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, 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 and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.
#LI-RA1