AI Engineer (Intern) at Juniper Square in Remote

Summary: Juniper Square is focused on unlocking the potential of private markets through technology. They are seeking an AI Engineer Intern to help design, build, and test AI-driven product features that will drive change in the industry. Responsibilities: - Collaborate with engineers and product managers to design and prototype AI-powered features that directly impact a key product area at the core of our business - Contribute to the development, training, and evaluation of machine learning and natural language processing (NLP) models using industry-standard tools and frameworks - Work with large and complex datasets - performing data preprocessing, feature engineering, and analysis to support model development - Apply foundational machine learning and deep learning techniques to solve real-world problems in the private markets space - Assist with integrating AI models into production systems, including testing, validation, and iteration in collaboration with the engineering team - Support ongoing research and experimentation efforts, particularly around NLP and large language models (LLMs), to drive innovation in product development - Partner closely with engineering, design, and product stakeholders to evaluate the technical feasibility and ensure alignment of proposed AI features with the overall product vision and strategy - Participate in regular team meetings, code reviews, and brainstorming sessions - bringing curiosity, ideas, and technical insight to the table - Receive mentorship from experienced AI and engineering leaders, while gaining exposure to company leadership and strategic initiatives - Contribute to documentation and knowledge sharing to help scale AI development efforts across the team - Take ownership of meaningful projects with the potential to influence long-term company strategy and product direction - Embrace a growth mindset - continuously learning new tools, methodologies, and concepts in the rapidly evolving field of AI Required Qualifications: - Currently pursuing a Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related quantitative field (e.g., Mathematics, Statistics, Physics) - Strong academic foundation with relevant coursework in algorithms, data structures, linear algebra, calculus, probability, and statistics - Completed or currently enrolled in courses in machine learning, deep learning, NLP, or computer vision (highly desirable) - Proficient in Python; experience with libraries such as TensorFlow, PyTorch, scikit-learn, NumPy, and Pandas is a plus - Familiarity with data manipulation, preprocessing, and feature engineering techniques - Exposure to databases (SQL/NoSQL) and data querying; experience with data visualization tools (e.g., Looker, Tableau, Power BI) is a bonus - Understanding of foundational ML algorithms (e.g., regression, decision trees, clustering) and the underlying mathematical concepts - A strong grasp of Large Language Model (LLM) mechanisms, including knowledge of sampling strategies, structured output generation, and function-calling capabilities. Hands-on experience with prompt engineering is a strong plus - Exposure to cloud platforms (e.g., AWS, GCP) and cloud-based ML services (e.g., Amazon SageMaker, Google AI Platform) is beneficial - Basic knowledge of software engineering practices, including version control (Git), Agile development, and API integration (REST, GraphQL) - Strong analytical and problem-solving skills with a high attention to detail - Eagerness to learn and apply new AI concepts in a fast-paced, evolving environment - Clear and effective communication skills, both written and verbal - Collaborative mindset with the ability to contribute meaningfully to team discussions and technical problem-solving - Self-starter with curiosity, initiative, and a proactive approach to innovation Preferred Qualifications: - Personal AI/ML projects (e.g., GitHub portfolio) - Participation in AI/ML competitions or hackathons - Research experience or academic publications in AI-related areas - Contributions to open-source projects Required Skills: Machine Learning, Natural Language Processing, Python Important Skills: Deep Learning, Data Manipulation, Large Language Models, SQL/NoSQL Nice-to-Have Skills: Analytical Skills, Curiosity