Senior Machine Learning Engineer
AI & Machine Learning
WFA Digital Insight
As the demand for skilled machine learning engineers continues to grow, with a 25% increase in job postings over the past year, roles like this one at Mercury are highly sought after. With the fintech industry expanding rapidly, companies are looking for experts who can develop and implement cutting-edge ML solutions to enhance customer experience and prevent financial crimes. Mercury stands out for its commitment to crafting an exceptional banking experience for startups, and this role is key to that mission. Before applying, candidates should be prepared to showcase their expertise in machine learning, data engineering, and their ability to work in a fast-paced, remote environment.
Job Description
About the Role
The Senior Machine Learning Engineer position at Mercury is a critical role that involves partnering with data science and engineering teams to design and deploy machine learning and general AI microservices. The primary focus of these services will be on automating reviews, which is essential for preventing money laundering and financial crimes. This role matters because it directly contributes to ensuring the safety and security of Mercury's banking experience for startups. The successful candidate will be part of a team that is passionately focused on creating a secure environment that meets the needs of customers, administrators, and regulators.What You Will Do
- Partner with data science and engineering teams to design and deploy ML and Gen AI microservices
- Work with a full-stack engineering team to embed these services into the overall review experience
- 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
- Collaborate on small to medium-sized projects, self-organizing when necessary
- Contribute to shaping and building Mercury's future
- Develop and maintain documentation for internal and external stakeholders
- Stay up-to-date with industry trends and emerging technologies in machine learning and data engineering
What We Are Looking For
- 7+ years of experience in machine learning engineering, data engineering, backend software engineering, and/or DevOps
- Expertise in a full modern data stack, including Snowflake, dbt, Fivetran, Airbyte, Dagster, and Airflow
- Strong skills in SQL, dbt, and Python
- Experience with OLAP/OLTP data modeling and architecture
- Knowledge of key-value stores such as Redis, DynamoDB, or equivalent
- Familiarity with streaming/real-time data pipelines like Kinesis, Kafka, or Redpanda
- Experience with API frameworks such as FastAPI, Flask, etc.
- Production ML service experience
- Ability to work across a full-stack development environment, with experience transferable to Haskell, React, and TypeScript
Nice to Have
- Experience working in a fintech or banking environment
- Knowledge of financial regulations and compliance
- Certification in machine learning or data engineering
- Experience with agile development methodologies
- Strong understanding of cloud computing platforms such as AWS or Google Cloud
Benefits and Perks
- Competitive base salary
- Equity in the form of stock options or RSUs
- Comprehensive benefits package
- Flexible working hours and remote work options
- Professional development opportunities
- Access to cutting-edge technologies and tools
- Collaborative and dynamic work environment
- Recognition and reward for outstanding performance
- Support for continuous learning and skill development
How to Stand Out
- Ensure you have a strong portfolio showcasing your experience with machine learning and data engineering, especially in automating reviews and working with full-stack engineering teams.
- Highlight your ability to work independently and as part of a team, with a strong sense of product ownership.
- Be prepared to discuss your experience with a full modern data stack and how you've applied it in previous roles.
- Showcase your knowledge of financial regulations and compliance, especially in the context of preventing money laundering and financial crimes.
- Demonstrate your ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Prepare examples of how you've implemented testing, observability, and disaster recovery for ML services in the past.
- Research Mercury's approach to fintech and banking for startups, and be ready to discuss how your skills and experience align with the company's mission.
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