Software Engineering (Generative AI) Intern
Lynx Analytics
πInternship - Singapore
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Job highlights
Summary
Join our innovative Data Engineering team as a Software Engineering Intern, contributing to cutting-edge Generative AI projects while gaining hands-on experience in software development.
Requirements
- Currently in the final year of a university degree / pursuing a postgraduate degree in Computer Science, Engineering, or a related field
- Proficient in Python for prototyping and testing analytical concepts
- Strong problem-solving abilities and analytical thinking
- Excellent verbal and written communication skills in English
- Available to start soon or immediately, with the ability to commit for at least 6 months
Responsibilities
- Contribute to the solutioning process through designing, developing and testing applications
- Write clean, maintainable and efficient code in Python
- Contribute to code reviews and team discussions on software design and architecture
- Conduct debugging and troubleshooting to resolve issues and optimise performance
- Provide comprehensive documentation of methodologies, modeling approaches, assumptions, and processes for future reference and reproducibility
Preferred Qualifications
- Experience with Large Language Models (LLMs) and Transformers is a plus (e.g., Hugging Face, AutoTrain, PyTorch, PyG, OpenAI, or other relevant APIs)
- Participation in Kaggle challenges, presentations at meetups, maintaining a software engineering-related blog or GitHub page, or involvement in software engineering volunteer work is advantageous
Benefits
- Collaborative Team Environment
- Independence to work on specific topics with coaching and mentorship from senior colleagues
- Gaining experience in Data Science and/or Artificial Intelligence
- An international working environment with colleagues of diverse nationalities across offices in Europe and Asia
- Flexible work hours and a hybrid work arrangement
Job description
SUMMARY STATEMENT
The Software Engineering Intern plays an operational role within our innovative Data Engineering team, actively contributing to cutting-edge Generative AI projects. This role offers a unique opportunity to gain hands-on experience in software development while contributing to Generative AI solutioning. The ideal candidate is curious, analytical, and eager to apply their skills in working with large language models and software engineering tools.
WHAT THIS INVOLVES
- Contribute to the solutioning process through designing, developing and testing applications.
- Write clean, maintainable and efficient code in Python.
- Contribute to code reviews and team discussions on software design and architecture.
- Conduct debugging and troubleshooting to resolve issues and optimise performance.
- Provide comprehensive documentation of methodologies, modeling approaches, assumptions, and processes for future reference and reproducibility.
SKILLS, QUALIFICATIONS AND EXPERIENCE
- Currently in the final year of a university degree / pursuing a postgraduate degree in Computer Science, Engineering, or a related field.
- Proficient in Python for prototyping and testing analytical concepts.
- Strong problem-solving abilities and analytical thinking.
- Excellent verbal and written communication skills in English.
- Available to start soon or immediately, with the ability to commit for at least 6 months
- Experience with Large Language Models (LLMs) and Transformers is a plus (e.g., Hugging Face, AutoTrain, PyTorch, PyG, OpenAI, or other relevant APIs).
- Participation in Kaggle challenges, presentations at meetups, maintaining a software engineering-related blog or GitHub page, or involvement in software engineering volunteer work is advantageous.
WHAT LYNX OFFERS
- Collaborative Team Environment
- Independence to work on specific topics with coaching and mentorship from senior colleagues
- Gaining experience in Data Science and/or Artificial Intelligence
- An international working environment with colleagues of diverse nationalities across offices in Europe and Asia
- Flexible work hours and a hybrid work arrangement
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