Machine Learning Research Engineer
@ High5

Hybrid
$150,000
Hybrid
Contractor
Posted 23 days ago

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Job Details

About High5

Join one of the largest and most dynamic Artificial Intelligence (AI) Talent Communities. High5 is a space where world-class engineers, researchers, and innovators collaborate, learn, and co-create the future of AI. If you are passionate about AI, machine learning, large language models, and next-generation intelligent systems, this is the place to grow, connect, and shape the future.

Machine Learning Research Engineer Overview

This role will design and test state-of-the-art machine learning models and algorithms, pushing the boundaries of ML research in areas like NLP, computer vision, and decision-making systems.

Key Responsibilities

  • Research, develop, and optimize machine learning algorithms
  • Design experiments, collect and prepare datasets, and evaluate results
  • Implement scalable ML pipelines with cross-functional teams
  • Investigate deep learning architectures such as CNNs, RNNs, and Transformers
  • Publish research insights and contribute to the AI community

Required Skills and Experience

  • Proficiency in Python, TensorFlow, or PyTorch
  • Strong background in mathematics, statistics, and probability
  • Experience with data preprocessing, model evaluation, and performance analysis
  • Knowledge in computer vision, NLP, or reinforcement learning is a plus
  • Strong research and problem-solving mindset

Key skills/competency

Machine Learning, Research, Python, TensorFlow, PyTorch, NLP, Computer Vision, Deep Learning, Algorithms, Data Analysis

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How to Get Hired at High5

🎯 Tips for Getting Hired

  • Customize your resume: Highlight AI projects and research experience.
  • Research High5's culture: Understand their mission and community impact.
  • Showcase technical skills: Emphasize Python, TensorFlow, and model design.
  • Prepare for interviews: Practice ML problem-solving and research insights.

📝 Interview Preparation Advice

Technical Preparation

Review Python coding exercises.
Practice TensorFlow or PyTorch projects.
Study deep learning architectures.
Build ML pipelines on datasets.

Behavioral Questions

Describe a challenging research project.
Explain your teamwork in cross-functional settings.
Discuss a time you solved a technical problem.
Share an experience of adapting to feedback.

Frequently Asked Questions