Applied Science: PhD Internship Opportunities - Multiple Locations

Analyze and improve performance of advanced algorithms on large-scale datasets and cutting-edge research in machine intelligence and machine learning applications. Think through the product scenarios and goals, identify key challenges, then fit the scenario asks into Machine Learning (ML) tasks, design experimental process for iteration and optimization. Implement prototypes of scalable systems in AI applications.

Gain an understanding of a broad area of research and applicable research techniques, as well as a basic knowledge of industry trends and share your knowledge with immediate team members. Prepare data to be used for analysis by reviewing criteria that reflect quality and technical constraints. Reviews data and suggests data to be included and excluded and be able to describe actions taken to address data quality problems. Assist with the development of usable datasets for modeling purposes and support the scaling of feature ideation and data preparation.

Help take cleaned data and adapt for machine learning purposes, under the direction of a senior team member Currently pursuing a Doctorate Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field. Must have at least one additional quarter/semester of school remaining following the completion of the internship. Candidate must be enrolled in a full time bachelor's, masters, MBA, or PhD program in area relevant for the role during the academic term immediately before their internship.

Explore product challenges using state of the art solutions. Research publications, coursework, or project experience relevant to search, language models, recommender systems, geospatial or location intelligence, or content and commerce systems. Experience running controlled experiments and interpreting offline and online evaluation metrics. Familiarity with large-scale distributed systems or productionizing applied science solutions.

Passion for building AI experiences and an understanding of how AI tools and technologies can be applied to improve relevance, discovery, personalization, and end-user satisfaction.