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Graduate Research Co-op – AI & Representation Learning
AncestryGraduate Research Co-op for AI & Representation Learning applying computational approaches at Ancestry. Support the development of models and collaborate with senior researchers on genomic data analysis.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Representation Learning and Large-scale Data Modeling, with proficiency in Python and modern ML frameworks like PyTorch and TensorFlow. Capable of developing scalable ML prototypes and efficient data pipelines for complex datasets, while collaborating effectively with researchers to document and report findings.
Highest-signal resume keywords
Representation LearningPython ProgrammingPyTorchTensorFlowData Pipeline Development
ATS Keywords
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Hard Skills
Representation LearningEmbedding ModelsLarge-scale Data ModelingPython ProgrammingData AnalysisMachine Learning PrototypingData ProcessingVectorizationModel EvaluationBenchmarking
Soft Skills
Analytical SkillsCollaborationDesire for Real-world Applications
Tools & Technologies
PyTorchTensorFlowVector DatabasesLarge Language Models
Industry Keywords
Machine LearningData ScienceHierarchical DataAutomated InsightsTechnical Reporting
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Support the development of representation learning models to integrate massive-scale hierarchical data with diverse record sets.
- Evaluate and benchmark diverse machine learning models to resolve data conflicts and improve the accuracy of relationship discovery at scale.
- Assist in building scalable ML prototypes that analyze billions of data points to provide automated insights and personalization.
- Develop efficient data pipelines to process and vectorize massive-scale datasets for downstream research and modeling tasks.
- Collaborate closely with senior researchers to document findings and prepare technical reports on model performance and scalability.
Requirements
What you’ll need- Currently enrolled in a Graduate program (Ph.D. preferred, or MS) in Computer Science, Data Science, or a related quantitative field.
- Solid understanding of Representation Learning, Embedding models, or Large-scale Data Modeling.
- Proficient in Python and modern ML frameworks such as PyTorch or TensorFlow; familiarity with Vector Databases or Large Language Models (LLMs) is a plus.
- Strong analytical skills with the ability to extract meaningful insights from massive-scale, complex datasets.
- A collaborative spirit and a desire to see research translated into real-world applications.
Benefits
Comp & perks- Flexible work arrangements
- Professional development opportunities