Inactive
J

Juniper Square

Unified platform for private investment partnerships

AI Engineer Intern

Applied Machine Learning +1Posted on Apr 28
Internship · Summer 2026$50 /hr

About the Job

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
  • 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 such as Looker, Tableau, Power BI is a bonus
  • Understanding of foundational machine learning algorithms (e.g., regression, decision trees, clustering) and the underlying mathematical concepts
  • A strong grasp of Large Language Model 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 (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)

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

Desired 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

About the Company

Juniper Square

Unified platform for private investment partnerships

Juniper Square provides end-to-end software for private investment partnerships, serving general partners and limited partners in commercial real estate, private equity, and venture capital. Its platform unifies fundraising, investor operations, and fund administration into a single system to manage investment data and workflows from start to finish. The product centralizes secure data sharing, tracks investor communications, automates tasks, and offers a 360-degree view of investment data to speed investment closes. It differentiates itself with a universal, purpose-built system for private markets that scales for firms of all sizes and structures, aiming to simplify and accelerate the private investment process for both GP and LP users.

Company size
1,001-5,000
Company stage
Series D
Total funding
$371M
Headquarters
San Francisco, CA
Founded
2014

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