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Applied Science: PhD Internship Opportunities - Multiple Locations is a role focused on analyzing and improving the performance of advanced algorithms on large-scale datasets within machine intelligence and machine learning applications. You will translate product scenarios into specific machine learning tasks, design experimental processes for optimization, and implement prototypes of scalable systems in AI applications. Responsibilities include preparing high-quality data for modeling, scaling feature ideation, and addressing technical constraints under senior guidance. The role requires expertise in research techniques related to search, language models, recommender systems, geospatial intelligence, or content and commerce systems. You will utilize skills in running controlled experiments, interpreting offline and online evaluation metrics, and navigating large-scale distributed systems. The work focuses on leveraging state of the art solutions to improve relevance, discovery, personalization, and end-user satisfaction through advanced AI tools.
Analyze and optimize performance of advanced machine learning algorithms on large-scale datasets.
Translate product goals into specific machine learning tasks and design experimental processes for optimization.
Implement prototypes of scalable systems for AI applications.
Prepare, clean, and curate high-quality datasets for modeling purposes.
Identify and resolve data quality issues while managing technical constraints.
Scale feature ideation and data preparation workflows.
Run controlled experiments and interpret offline and online evaluation metrics.
Research and apply state-of-the-art solutions to improve personalization and user discovery.
What we're looking for
Currently pursuing a Doctorate Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field.
Must have at least one additional quarter/semester of school remaining following the completion of the internship.
Must be enrolled in a full-time bachelor's, masters, MBA, or PhD program in a relevant area during the academic term before the internship.
Experience with research publications, coursework, or projects related to search, language models, recommender systems, geospatial 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.
Ability to design experimental processes for iteration and optimization of machine learning tasks.
Proficiency in preparing, cleaning, and scaling datasets for machine learning modeling purposes.
Machine Learning
Large Language Models
Diffusion Models
Reinforcement Learning
Python
Swift
Objective C
Java
TensorFlow
PyTorch
CoreFlow
Linear Algebra
Statistics
Time Series Analysis
Optimization
Causal Inference
Stochastic Modeling
Python
Swift
Objective C
Java
TensorFlow
PyTorch
CoreFlow
Machine Learning
Large Language Models
Diffusion Models
Reinforcement Learning
Linear Algebra
Statistics
Time Series Analysis
Stochastic Modeling
Optimization
Causal Inference
Python
Scala
Java
C++
SQL
Spark
PyTorch
TensorFlow
scikit-learn
LLM
Generative AI
Machine Learning
Data Modeling
Software Development Lifecycle
Data Structures