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Machine Learning Engineering Intern

Remote
12-16 weeks
Internship
PythonPyTorchTensorFlowTransformersMLOpsCUDA

About the Role

Join our AI research team as a Machine Learning Engineering Intern and work on cutting-edge projects that advance the state of artificial intelligence. You'll be involved in developing, training, and deploying large-scale ML models while contributing to groundbreaking research in areas like consciousness, long-term planning, and AI safety.

This role offers a unique opportunity to work with leading researchers and engineers, gaining hands-on experience with the latest ML frameworks, distributed training systems, and model optimization techniques.

Key Responsibilities

  • • Design and implement neural network architectures for various AI research projects
  • • Develop and optimize training pipelines for large language models and multimodal systems
  • • Implement state-of-the-art algorithms from recent research papers
  • • Build MLOps infrastructure for model versioning, monitoring, and deployment
  • • Conduct experiments and analyze results to advance research objectives
  • • Optimize model performance for both training efficiency and inference speed
  • • Collaborate with researchers to translate theoretical concepts into practical implementations

Requirements

Essential Requirements:

  • • Currently pursuing a degree in Computer Science, Machine Learning, Mathematics, or related field
  • • Strong programming skills in Python and experience with ML frameworks (PyTorch/TensorFlow)
  • • Solid understanding of machine learning fundamentals and deep learning concepts
  • • Experience with neural network training and optimization techniques
  • • Familiarity with linear algebra, statistics, and calculus

Preferred Qualifications:

  • • Experience with transformer architectures and large language models
  • • Knowledge of distributed training and GPU programming (CUDA)
  • • Familiarity with MLOps tools and cloud platforms (AWS, GCP)
  • • Research experience or publications in machine learning
  • • Experience with reinforcement learning or multi-agent systems

What You'll Learn

  • • Advanced neural network architectures
  • • Large-scale model training techniques
  • • Research methodology and experimentation
  • • MLOps and production deployment
  • • GPU optimization and distributed computing
  • • AI safety and alignment principles

Internship Details

Duration: 12-16 weeks (Summer/Fall 2024)

Location: Remote

Start Date: Flexible based on availability