We are seeking remote PhD interns for Summer 2026!

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As an intern, you will help develop cutting-edge evaluation methodologies for AI systems. Your research will focus on creating robust, scalable metrics and frameworks to assess the quality, consistency, and performance of generative models across multiple modalities. You may contribute in one or more of the following areas:

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• Novel Evaluation Metrics: Develop innovative assessment methodologies for emerging AI capabilities, focusing on consistency and quality across complex multi-modal outputs
• Self-Improving Assessment: Design evaluation systems that learn and adapt from feedback, automatically discovering new evaluation criteria and improving assessment quality over time
• Privacy-Preserving Evaluation: Design frameworks that incorporate domain-specific implementations of differential privacy to protect sensitive user information while maintaining utility for model training and assessment.
• Ethical Fair Housing Evaluation: Develop scalable methodologies for assessing agentic systems, ensuring compliance with fair housing standards and promoting ethical, responsible AI deployment

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This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.

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In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $104,000.00 - $166,000.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $104,000.00 - $166,000.00 annually. The base pay range is specific to these locations and may not be applicable to other locations.

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Who you are

• Currently enrolled as a PhD student in computer science, machine learning, computer vision, or a related field, with strong publication record
• Candidates should have a background in one or more of the following areas:
  • Evaluation methodologies for AI/ML systems
  • Computer vision metrics and 3D consistency assessment
  • Generative model evaluation (text, image, video, 3D)
  • Multi-modal assessment and automated feedback systems
  • Knowledge of data privacy methods (e.g., differential privacy, federated learning, secure ML) and their application.
  • Single agent or multi-agent system evaluations
• Familiarity with modern deep learning frameworks (e.g., PyTorch, Hugging Face Transformers)
• Strong research mindset, with motivation to publish
• Interest in applying AI to complex, multi-stakeholder domains
• A record of publication in conferences, workshops, or journals is a plus  

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