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
Requirements:
- 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
- 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