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Eudia

AI Engineer Intern

Palo Alto, CAinternshipinternAdded 3 days ago

About this role

Eudia is seeking an AI Engineer Intern for spring or summer to work on machine learning and generative AI challenges in the legal domain. You'll collaborate with experienced engineers to build, evaluate, and optimize AI models for enterprise legal applications, gaining hands-on experience with production-grade AI systems.

What you'll do

  • Design and implement AI/ML solutions for customer-specific legal challenges
  • Build, test, and deploy machine learning models in production environments
  • Evaluate and fine-tune open-source and closed-source models
  • Troubleshoot and optimize AI models to address performance issues and model drift
  • Conduct research to improve model efficiency, accuracy, and retrieval quality
  • Develop documentation for AI workflows and deployment processes

What they're looking for

  • Machine learning and deep learning
  • LLMs and generative AI
  • Retrieval-Augmented Generation (RAG)
  • Natural Language Processing (NLP)
  • MLOps tools and practices (MLflow)
  • Python or ML frameworks
  • Data analysis and predictive modeling
  • Cloud platforms and enterprise systems

Benefits

  • Work on cutting-edge AI innovation in legal technology
  • Learn from experienced AI researchers and engineers
  • Contribute to real-world, enterprise-grade AI applications
  • Professional growth and exposure to new AI techniques
  • Dynamic, mission-driven team culture
  • Location in Palo Alto with access to Silicon Valley network
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Eudia

Eudia builds AI-powered legal technology platforms designed for Fortune 500 companies and government organizations. The company is hiring Forward Deployed Engineers to work directly with customers on AI solution deployment, and Frontend Engineers to develop scalable web applications that integrate generative AI systems.

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Likely interview questions

  • Describe your experience with large language models and RAG systems—what challenges have you encountered and how did you address them?
  • How have you approached evaluating and fine-tuning machine learning models in previous projects?
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