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About the Role

Georgian's R&D team partners directly with portfolio companies, providing technical horsepower and strategic guidance to accelerate their AI initiatives. This role offers the opportunity to work on diverse, cutting-edge AI problems across multiple high-growth companies with the support of a leading VC firm, tackling fascinating challenges and helping to unearth the next generation of industry leaders.

Responsibilities

• Support AI system development, including building LLM agents, RAG pipelines, and fine-tuned domain-specific models.
• Implement workflows using frameworks like LangChain, LlamaIndex, and PydanticAI.
• Contribute to portfolio projects by supporting Georgian 'strike teams' embedded in portfolio companies.
• Assist with code implementation, testing, and documentation for portfolio projects.
• Convert technical concepts into clear documentation.
• Support the preparation of demos and presentations for stakeholders.
• Assist in rapid prototyping, running experiments and ablation studies.
• Contribute to internal tech notes and documentation.
• Support AI roadmap development by gathering requirements in discovery workshops.
• Assist in documenting technical specifications and implementation plans.
• Assist in implementing evaluation pipelines and testing frameworks to ensure model reliability and compliance.

Requirements

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Electrical Engineering, Statistics, or a related quantitative discipline with a focus on machine learning, optimization theory, or related areas.
• Familiarity with deep learning toolkits such as Scikit-learn, TensorFlow, or PyTorch.
• Understanding of GenAI, LLM tooling, and Context Engineering fundamentals.
• Proficiency in Python and foundational software development skills.
• Ability to understand applied research problems, assist in designing experiments, implement solutions, and clearly document findings.
• Demonstrated ability to work on team projects and collaborate effectively with technical groups.
• Ability to communicate progress to mentors and stakeholders.
• Experience contributing to open-source AI/ML-related projects or research papers (Nice-to-Have).
• Familiarity with cloud platforms (AWS, GCP, Azure) for MLOps and model deployment (Nice-to-Have).
• Exposure to specific industry verticals relevant to Georgian's portfolio (Nice-to-Have).
• High ambition and agency; thrive in low-structure, high-ownership settings.
• Insatiable curiosity; ability to debug root causes.
• Team-oriented mindset; amplify colleagues and founders.
• Ability to perform commercial storytelling, speaking with brevity and distilling complexity to show value.

Skills

• Machine Learning
• Optimization Theory
• Deep Learning Toolkits (Scikit-learn, TensorFlow, PyTorch)
• GenAI
• LLM Tooling
• Context Engineering
• Python
• Software Development
• Problem Formulation
• Experimental Design
• Documentation
• Communication
• Collaboration
• Cloud Platforms (AWS, GCP, Azure)
• MLOps
• Model Deployment

Work Type

• Internship

Experience Level

• Intern

Education Level

• Bachelor's Degree
• Master's Degree

About the Company

• Georgian is a growth equity firm that invests in B2B technology companies, supporting portfolio companies with capital and technical capability.
• Active in analytics and applied AI since 2008, investing across the AI technology stack.
• In-house AI Lab with 10+ engineers and scientists assists portfolio companies with production AI deployment.
• Team comprises investors, machine learning professionals, software entrepreneurs, and experienced operators.
• Manages $5.8B AUM (as of December 31, 2025) and has invested in over 80 companies.
• Mission is to empower the world's leading B2B software companies with an unfair advantage, accelerating their participation in the digital economy.

Equal Opportunity

• Georgian is dedicated to building an inclusive environment where everyone can thrive.
• Provides reasonable accommodations throughout the recruitment process.
• Uses artificial intelligence (AI) to support parts of the hiring process, such as screening and evaluating applications, with human decision-making for all final hiring decisions.
• Regularly reviews AI tools to promote fairness and reduce the risk of unintended bias.