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Machine Learning Engineer (MLE) Intern

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20d ago

🚀 Summer Internship


⌛ Closed
Applications are closed

Summer Internship



  • Our formula for success is to hire exceptional people, encourage their ideas and reward their results.
  • We are seeking a motivated Machine Learning Engineer (MLE) Intern to join our team and play a crucial role in developing cutting-edge NLP solutions for finance and trading applications. This internship provides an exceptional opportunity to gain hands-on experience with large language models (LLMs), machine learning model development, and the fundamentals of machine learning operations (MLOps) while actively contributing to the growth of our software engineering environment.


  • A degree in Computer Science, Data Science, Engineering, or a related field graduating between December 2024 and June 2025 (Bachelor’s, Master’s)
  • Strong foundation in machine learning concepts (supervised/unsupervised learning, classification, regression).
  • Solid understanding of NLP fundamentals and experience with LLMs a plus.
  • Proficiency in Python programming and familiarity with libraries like NumPy, Pandas, and Scikit-learn.
  • Excellent analytical, problem-solving, and communication skills.
  • Passion for developing innovative AI solutions within the financial domain.
  • Preferred Skills:
  • Experience with NLP tasks (text classification, summarization, question-answering).
  • Knowledge of TensorFlow or PyTorch.
  • Basic understanding of MLOps principles (monitoring, versioning, model serving).
  • Learning Opportunities:
  • Gain in-depth experience with cutting-edge NLP techniques, effective LLM utilization, and model deployment in a real-world setting.
  • Develop robust MLE skills, from data engineering to model evaluation, while directly influencing the growth of the SWE environment.
  • Contribute to projects with direct impact on financial decision-making.
  • Be a part of building a strong software engineering culture, driving improvements in code quality, collaboration, and efficiency.

Education requirements

Currently Studying

Area of Responsibilities



  • Model Development & Refinement: Design, implement, and refine NLP models for financial applications, leveraging LLM capabilities and collaborating to improve software engineering practices.
  • Prompt Engineering: Develop and optimize prompts for more effective interaction with LLMs, tailored to finance-specific tasks, in alignment with evolving SWE methodologies.
  • Data Engineering: Participate in building data pipelines for model training and evaluation, including data collection, cleaning, preprocessing, and labeling, while contributing to better data management.
  • Model Testing & Evaluation: Design and implement rigorous testing frameworks to assess NLP model performance and identify areas for improvement.
  • Python Development: Write efficient Python scripts for data manipulation, model training, and model deployment, with a focus on code quality and maintainability.
  • SWE Collaboration: Work closely with the team to establish and refine software engineering practices, promoting code reviews, testing, and documentation.
  • Research & Learning: Stay updated on the latest NLP and MLOps techniques, share insights and actively bring improvements to development processes


Work type

Full time

Work mode