About the job
Applied Machine Learning Engineer - Media Search and Recommendation. The Search and Personalization team owns the core discovery experiences on Bloomberg.com and the Bloomberg mobile app. We are responsible for how users search for, discover, and engage with content across a global audience, with a strong focus on personalization and recommendations. By powering Bloomberg’s discovery hub, we play a critical role in helping users find the content that matters most to them, ensuring experiences are relevant, timely, and high quality. Our systems operate at web scale and sit at the intersection of backend services, data pipelines, and user-facing experiences. We are looking for a dedicated and experienced Machine Learning engineer to join the team. The ideal candidate will have a passion for Machine Learning, building distributed web scale systems and partnering with a variety of stakeholders within our expansive organization.
Responsibilities
· Lead the design and delivery of personalization and recommendation systems that power discovery across Bloomberg.com and mobile platforms.
· Build and maintain data pipelines that support personalization, experimentation, and model-driven product experiences.
· Design, develop, and evolve backend services and APIs that deliver personalized content at scale with low latency.
· Partner closely with the data science team to productionize models and enable rapid experimentation.
· Collaborate across the stack, including with frontend engineers, to deliver cohesive and high-quality user experiences.
· Make architectural decisions, drive technical discussions, and help set technical direction for the personalization platform.
· Mentor other engineers and raise the overall technical bar of the team.
Qualifications
Minimum
· 5+ years of industry experience in an Object Oriented Programming language, preferably Python or Java
· 5+ years of industry experience working with large data sets, performing machine learning model experimentation, implementation and deployment.
· A Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent professional experience.
Preferred
· Exposure building and developing a content recommendation system, focusing on recommendation algorithms and machine learning capabilities
· Familiarity building user behavioral models to predict users likelihood to perform high value actions like propensity to subscribe or churn
· Experience with end-to-end machine learning projects, including data analysis, feature engineering, model selection, hyper-parameter tuning, model deployment and ongoing performance monitoring.
· Hands-on expertise with big data systems and distributed data processing technologies such as Spark
· Proven track record building and maintaining data pipelines that power customer-facing product features.
· Demonstrated leadership delivering machine learning projects from inception through delivery.
· Practical expertise with public cloud platforms such as AWS.
· Proven ability to operate and evolve web-scale distributed systems in production.