Bluesheets – Machine Learning Intern

About the role 

We are seeking a highly skilled Machine Learning Engineer with expertise in Large Language Model (LLM) training and architecture. The ideal candidate should possess a strong background in natural language processing (NLP), deep learning, and be well-versed in the development and deployment of Specialized Language Models (SLM) and Multimodal Models (MML). The successful candidate will play a pivotal role in advancing our cutting-edge language technology solutions.

Key Responsibilities

• Work closely with our machine learning team to develop and implement machine learning models and algorithms to solve real-world problems.
• Collect, clean, and preprocess data for use in machine learning models, ensuring data quality and accuracy.
• Experiment with different machine learning techniques and algorithms, and evaluate their performance using appropriate metrics and evaluation methods.
• Collaborate with cross-functional teams to integrate machine learning models into existing products or systems, and iterate on model improvements based on feedback and results.
• Stay up-to-date on the latest developments and trends in machine learning and artificial intelligence, and contribute to the continuous improvement of our machine learning capabilities.
• Document and communicate your findings, insights, and recommendations to stakeholders through written reports, presentations, and data visualizations.

Qualifications:

• Currently pursuing or recently completed a degree in computer science, data science, statistics, or a related field.
• Strong understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Proficiency in programming languages commonly used in machine learning, such as Python or R.
• Experience with machine learning libraries and frameworks, such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Excellent analytical and problem-solving skills, with the ability to interpret and analyze complex datasets.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment and communicate technical concepts to non-technical stakeholders.