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Machine Learning Research Engineer
High5
HybridHybrid
Original Job Summary
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
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
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Review Python coding exercises.
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Practice TensorFlow or PyTorch projects.
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Study deep learning architectures.
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Build ML pipelines on datasets.
Behavioral Questions
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Describe a challenging research project.
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Explain your teamwork in cross-functional settings.
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Discuss a time you solved a technical problem.
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Share an experience of adapting to feedback.