S1E1: Meta's senior machine learning engineer Katerina Iliakoupoulou on leaving your dream job

Career development as an Individual Contributor (IC) requires building deep domain expertise and technical skills while maintaining strong communication abilities to demonstrate impact

Successful machine learning projects require quick prototyping, data-driven validation, and the ability to convince stakeholders through solid arguments backed by metrics

When working with ML at scale, carefully consider resource constraints and compute costs - solutions need to demonstrate enough impact to justify their resource usage

Hybrid work environments can affect productivity differently - office time can be valuable for focused deep work and collaboration, while remote days work well for heads-down tasks

Building reusable, generalizable ML solutions that can be adopted across different teams creates more impact than single-use implementations

Good managers listen to their team members, support their goals, provide growth opportunities, and help them align priorities with business objectives

Staying current with ML developments requires dedicated time for reading papers, research, and understanding new technologies while filtering out distractions

Cross-functional collaboration and getting stakeholder buy-in is critical for ML projects - solutions need to demonstrate clear value to product teams

ML engineers need to balance building technically sophisticated solutions with practical business constraints and impact metrics

In modern tech companies, recommendation systems and personalization are evolving from passive content consumption to more interactive, personalized experiences