Role Overview
We’re looking for a highly motivated Machine Learning Intern to help build and deploy impactful AI-driven solutions. You’ll work end-to-end on real-world ML problems, collaborate with cross-functional partners, and apply cutting-edge techniques—including Large Language Models—to power new product capabilities.
What You’ll Do
- Own the ML lifecycle: take models from data exploration and feature engineering through training, evaluation, deployment, and ongoing monitoring
- Build with state-of-the-art AI: apply modern ML techniques, including Large Language Models (LLMs), to solve novel problems and unlock new customer experiences
- Choose the right approach: leverage a range of methods such as deep learning, gradient boosting, and causal inference depending on the problem
- Run rigorous experiments: measure impact using A/B testing and sound statistical analysis
- Collaborate cross-functionally: partner with product managers and business stakeholders to turn models and insights into actionable strategy and user-facing features
What You’ll Need
- Graduate student status: currently pursuing an M.S. or Ph.D. in Data Science, Computer Science, Mathematics, Physics, Economics, Statistics, or a related quantitative field, with an expected graduation date between December 2026 and 2027
- Strong ML fundamentals: solid grounding in machine learning, statistics, probability, and optimization
- Python proficiency: experience with common data science and ML libraries such as pandas, NumPy, scikit-learn, and PyTorch
- SQL experience: ability to work with large datasets in modern data warehouses (e.g., Snowflake, BigQuery, Redshift, ClickHouse)
- Hands-on ML experience: demonstrated experience curating datasets and building, evaluating, and iterating on ML models
- Interest in applied AI: curiosity and motivation to integrate cutting-edge LLMs and agent-based systems into real-world solutions
- Strong communication skills: ability to clearly explain complex ideas to both technical and non-technical audiences
- Bias for action: comfort navigating ambiguity and a desire to ship, learn, and iterate quickly
Nice to Have
- Demonstrated passion for ML: publications, personal projects, internships, or prior experience applying AI/ML in practice
- Production-minded development: familiarity with software engineering best practices for ML, including Git, testing, and maintainable code
- Data orchestration experience: exposure to modern workflow and orchestration tools such as Airflow, Dagster, Prefect, or Metaflow