
Trusted by 45M users and growing - the best local & breaking news source in the US, featuring local weather, alerts, deals, events and more.

Trusted by 45M users and growing - the best local & breaking news source in the US, featuring local weather, alerts, deals, events and more.
Core product: Local news and information mobile app aggregating local publishers and contributor network
Users: Tens of millions of U.S. users (company-cited ~40–45M)
Founded: 2015 in Silicon Valley (legal name: Particle Media, Inc.)
Recent major funding: Series C $115M led by Francisco Partners (announced Jan 7, 2021)
Local news discovery and distribution; filling local news gaps via aggregation and contributor network.
2015
Local news / news aggregation / media
$115 million
Company press release announcing Series C led by Francisco Partners; press release states prior capital included more than $36M from earlier investors including IDG Capital.
“Francisco Partners led a $115M Series C and took a board seat; prior investors reported include IDG Capital.”
As a Machine Learning Engineer specializing in recommendation systems, you will play a pivotal role in shaping and enhancing our personalized content delivery platform. Your focus will be on developing and optimizing the machine learning components that power the core of our recommendation system, especially on modeling, feature engineering, data pipeline and business metric optimization, ultimately driving user engagement and satisfaction. Responsibilities include innovating and advancing recommendation models based on user interactions to improve user experience; utilizing machine learning algorithms to analyze and predict user preferences, engagement patterns, and content consumption behaviors, incorporating cross-platform data for comprehensive insights; optimizing and fine-tuning algorithms for improved accuracy, relevance, and user experience; proactively identifying and addressing any issues related to data quality, model drift, or system performance; designing, implementing, and optimizing robust and scalable data pipelines to collect, process, and store large volumes of user behavior data; collaborating with cross-functional teams to ensure seamless integration of data pipelines with existing systems and databases; developing and implementing innovative feature engineering techniques to extract meaningful insights from raw data; working closely with data scientists and other engineering teams to identify and create relevant features that improve the performance of our recommendation models. Requirements include a Master's or Ph.D. in Computer Science, Machine Learning, or a related field; minimum of 3 years’ industry experience in development, with in-depth knowledge of machine learning technologies (with a focus on recommendation system); strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch) and tools for data processing (e.g., Apache Spark); a strong passion for emerging technologies and a proven track record in analytical and problem-solving skills; excellent communication, teamwork, and project management skills; resilience and determination to elevate our business to new heights.