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AI Research Engineer

High5

HybridHybrid

Original Job Summary

About the Community

Join one of the largest and most dynamic Artificial Intelligence (AI) Talent Communities where world-class engineers, researchers, and innovators collaborate, learn, and co-create the future of AI.

Overview

As an AI Research Engineer at High5, you will explore, prototype, and implement novel algorithms and architectures that push the boundaries of AI. You will be part of a team committed to advancing capabilities in machine learning and deep learning.

Key Responsibilities

  • Conduct research on new AI models and training methodologies.
  • Develop prototypes and evaluation frameworks for innovative approaches.
  • Collaborate with research leads to translate concepts into production-ready systems.
  • Publish papers, present findings, and contribute to the research community.
  • Stay updated with advancements in AI, machine learning, and computational methods.

Required Skills

  • Strong academic or research background in Computer Science, AI or related fields.
  • Proficiency in deep learning frameworks and data analysis tools.
  • Understanding of mathematical modeling, probability, and statistics.
  • Familiarity with Python, NumPy, Pandas, and modern AI libraries.
  • Ability to design and interpret experiments scientifically.

Ideal Profile

An inquisitive and research-focused professional who thrives at the intersection of theory and practical innovation.

Key skills/competency

AI, Machine Learning, Deep Learning, Research, Python, Prototyping, Data Analysis, Algorithms, Innovation, Experimentation

How to Get Hired at High5

🎯 Tips for Getting Hired

  • Research High5 culture: Explore their mission, community values, and recent projects.
  • Customize your resume: Highlight AI research and deep learning skills.
  • Network online: Connect on LinkedIn and GitHub with team members.
  • Prepare for interviews: Focus on technical AI and research methodologies.

📝 Interview Preparation Advice

Technical Preparation

Review deep learning frameworks documentation.
Practice AI algorithm design exercises.
Work on prototype projects.
Study statistical models and Python libraries.

Behavioral Questions

Describe a challenging research project.
Explain teamwork during a prototype development.
Discuss handling experimental failures.
Share a learning experience from research.