Senior Software Engineer - Machine Learning

FIS - Seattle, WA

Full-time - Mid Level

Hybrid - Seattle, WA

Credit Intermediation and Related Activities

About the position

As the world works and lives faster, FIS is leading the way in fintech solutions that touch nearly every market, company, and person on the planet. Our teams are inclusive and diverse, working together to celebrate our achievements. We are looking for a Senior Machine Learning Engineer to join our Fraud Intelligence team, which is at the forefront of data science and machine learning technology. This team is dedicated to detecting and preventing fraud on a global scale, processing more than $40 trillion per year and enabling 95% of the world's leading banks. In this role, you will tackle complex challenges such as identity theft, credit card fraud, and money laundering. The technology you develop will protect individuals, businesses, and financial institutions from fraudsters, including organized crime rings. The fraud prevention space is fast-paced and rapidly evolving, requiring you to work cross-discipline with data scientists, analytics, product teams, and more. We seek candidates who not only possess strong technical skills but also have a passion for solving complex problems and a willingness to learn new skills along the way. You will be responsible for building a brand-new financial technology platform for the future, operating with ownership, integrity, and empathy. We encourage applicants with diverse perspectives and those who are not afraid to challenge assumptions. The position offers a hybrid work environment in our Seattle/Bellevue office, unless business needs dictate otherwise.

Responsibilities

• Understand business objectives, product requirements and develop ML algorithms that achieve them.
• Build prototypes and proof of concepts to determine feasibility, then drive data-based decisions.
• Run experiments to assess performance and improvements.
• Provide ideas and alternatives to drive a product/feature.
• Define data and feature validation strategies.
• Deploy models to production systems and operate them including monitoring and troubleshooting.
• Design, build, and manage the data pipelines and infrastructure that collect, store, and process large volumes of transactional and customer data from various sources.
• Develop, deploy, and scale machine learning models and applications in production and lower environments.
• Ensure data quality, security and availability for the data, notebooks, models, experiments, and applications.
• Integrate ML models with the SaaS platform and other services and tools, such as the model registry, feature store, data lake, and event streams.
• Collaborate with data scientists to develop and test machine learning models.
• Drive code reviews to ensure code quality, maintainability, and adherence to coding standards.
• Provide live on-call support by participating in the team on-call rotation and owning production issues from root cause analysis to resolution to future prevention.
• Partner with cross-functional teams (engineering, product, design, security, compliance etc.) to bring ideas to life.
• Build secure, robust, scalable, and performant systems for processing transactions and managing customer data.

Requirements

• At a minimum, a Bachelor's in CS or equivalent education and either 2+ years of relevant professional experience or advanced degree such as a master's or PhD.
• Experience leading projects from architectural design to production, while setting and maintaining high standards of technical excellence across your team.
• Effective communication and collaboration skills, and a history of collaborating effectively with your team and cross-functional stakeholders.
• Excellent communication and cross-functional collaboration skills to thrive in a fast-paced environment.
• Experience with data management, data, and build pipelines.
• Experience with building and deploying machine learning models.
• Experience with AWS, Snowflake, Databricks or similar technologies.

Nice-to-haves

• Typical qualifications for the role are 5+ years of relevant professional experience or a combination of work experience and advanced education.
• Deep expertise in at least one area of Machine Learning and AI.
• Experience with financial services data sources.
• Experience with MLflow and Feast or other Feature Stores is helpful.
• Proficiency in modern development frameworks and languages (e.g., Java, Python, Go).
• Proven ability to self-direct your technical work and scope projects effectively.
• Experience leading and mentoring junior engineers.
• Excellent communication and collaboration skills to influence both technical and non-technical stakeholders.
• Experience with cloud platforms (AWS, Azure, GCP).
• Experience with version control systems (Git), and DevOps practices like continuous integration and continuous delivery (CI/CD).
• A strong understanding of security best practices for building enterprise applications.

Benefits

• Opportunities to innovate in fintech.
• Tools for personal and professional growth.
• Inclusive and diverse work environment.
• Resources to invest in your community.
• Competitive salary and benefits.