Lead Machine Learning Engineer
OpenTeamsFull Time
Senior (5 to 8 years)
Candidates should have 7+ years of experience in machine learning engineering, data engineering, backend software engineering, or devops. Expertise is required in a full modern data stack including Snowflake, dbt, Fivetran, Airbyte, Dagster, and Airflow. Proficiency in SQL, dbt, Python, OLAP/OLTP data modeling, key-value stores (Redis, DynamoDB), streaming data pipelines (Kinesis, Kafka, Redpanda), and API frameworks (FastAPI, Flask) is essential. Experience with production ML services and full-stack development, transferable to Haskell, React, and TypeScript, is also necessary.
The Senior Machine Learning Engineer will partner with data science and engineering teams to design and deploy ML and Gen AI microservices, focusing on automating reviews. They will work with full-stack teams to integrate these services into the review experience, including human-in-the-loop processes and feedback loops. Responsibilities include implementing testing, observability, alerting, disaster recovery, tracing, performance, and regression testing for all services. The role also involves taking ownership of products and actively shaping Mercury's future through self-organization on projects.
Banking services for startups and founders
Mercury provides banking services specifically designed for startups, regardless of their size or stage of development. Their offerings include free checking and savings accounts, debit and credit cards, and options for domestic and international wire transfers, as well as treasury and venture debt services. The platform is user-friendly, allowing founders to manage their finances with ease. What sets Mercury apart from traditional banks is its focus on the startup community, offering programs that connect founders with valuable resources and advice to help them succeed. The goal of Mercury is to empower startups by providing them with the financial tools and support they need to grow and thrive.