Machine Learning Engineer • Mid-Level

Who you are

You have industry experience as a Machine Learning Engineer, working on a variety of classification and optimization problems. Your expertise includes areas such as payment fraud detection, click-through rate prediction, and collaborative filtering. You are comfortable applying machine learning techniques to large-scale social data and prediction challenges.

You possess strong programming skills, particularly in Python, and have a solid understanding of machine learning frameworks and libraries. Your analytical mindset allows you to approach complex problems with innovative solutions, and you thrive in collaborative environments where you can share your knowledge and learn from others.

What you'll do

In this role, you will join Meta's engineering team and focus on developing machine learning models that address significant challenges in social data. You will work on projects that involve classification tasks, such as spam detection and search ranking, and optimization problems that enhance user experience across Meta's platforms.

You will collaborate closely with cross-functional teams, including data scientists and software engineers, to design and implement scalable machine learning solutions. Your responsibilities will include conducting experiments, analyzing results, and iterating on models to improve performance.

You will also be involved in mentoring junior engineers and contributing to the overall growth of the machine learning team. Your insights will help shape the direction of future projects and ensure that Meta remains at the forefront of machine learning innovation.

What we offer

Meta provides a dynamic work environment where you can engage with cutting-edge technologies and tackle some of the most exciting challenges in the industry. You will have access to resources and support that foster your professional development and growth.

We encourage you to apply even if your experience doesn't match every requirement. At Meta, we value diverse perspectives and are committed to building an inclusive team that reflects the communities we serve.