About Gen Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.

Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.

When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs.

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen. About the Role

The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!

We are looking for a Machine Learning Engineer who is technically proficient in programming to build machine learning systems and maintain high performing machine learning models in production. Designing machine learning systems requires that you, as the machine learning engineer, is able to frame business problems and design machine learning solutions that can effectively bring value to the company. You will prototype, implement and experiment with machine learning methods to solve business use cases. You will lead efforts to automate, architect and optimise machine learning processes and workflows. You will also help define, execute and enforce best practices for test-driven model development processes for the entire Data Science team in MoneyLion.

Key Responsibilities

• Own and lead the end-to-end architecture, orchestration, and delivery of machine learning systems and pipelines, from problem framing to production
• Lead technical discussions with MLOps Engineers and Data Scientists to drive sound system design decisions
• Research, build and design robust, scalable machine learning systems and models that deliver measurable business value
• Design, execute and monitor machine learning model experiments and metrics; drive conclusions and recommendations independently
• Monitor, optimise and maintain machine learning solutions in production; proactively identify and resolve systemic issues
• Champion and enforce test-driven development and engineering best practices to reduce technical debt across ML systems
• Mentor junior ML engineers, providing guidance, code review feedback, and supporting their technical growth About You
• Proven track record of developing, deploying and owning machine learning models in production at scale
• Adept at problem solving and troubleshooting using both textbook methods and novel viewpoints; able to navigate ambiguity independently
• Proficient in Python and SQL
• Proficient with machine learning libraries and frameworks; hands-on experience with tools such as scikit-learn, XGBoost, or LightGBM is preferred
• Solid experience building machine learning systems, workflows and pipelines end-to-end
• Familiarity with MLOps tools such as DVC, MLFlow, Metaflow, Seldon, or BentoML is a strong advantage
• Hands-on experience with AWS, Docker, Kubernetes
• Strong communication, collaboration, and stakeholder management skills
• Demonstrated ability to mentor others and contribute to team-wide engineering culture

What’s Next

• TA Screening Call
• Take-Home Assessment
• Interview & discussion of Take-Home Assessment with the Hiring Manager (F2F)