Technical Intern (Masters or PhD)

CentificRedmond, WA

About The Position

Centific is seeking entry-level Applied Scientists through a student worker program. The ideal candidate will possess strong Python coding skills, a passion for AI quality, a strong ability to learn, and hands-on experience in delivering exceptional AI experiences. This role involves staying updated on AI community developments, integrating LLMs into production systems, collaborating on tools to improve AI Quality, leveraging data for innovative solutions, and communicating work to stakeholders.

Requirements

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • 3 + years’ experience coding in Python and experience in natural language processing or deep learning.
  • 1+ years' experience in a deep learning library, such as PyTorch or TensorFlow

Responsibilities

  • Keeping a keen eye on the latest developments in the broad AI community.
  • Working with science, engineering, and program management teams to integrate the latest large language models (LLMs) such as OpenAI models and open-source models into production systems.
  • Collaborating with other researchers, data scientists, product managers, and engineers in the team to build tools and improve the end-to-end AI Quality of our production systems.
  • Leveraging data from various sources and domains to design and implement innovative solutions that achieve rigorous metrics and address customer needs.
  • Effectively communicating and demonstrating work to stakeholders.
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