(MSc/PhD) AI Research Intern - LLMs, Causal Inference & Decision Making

• Build and experiment with LLMs, representation models, and modern ML systems for causal and decision-making problems

• Work with large-scale experimental data to understand how users respond to different interventions

• Build models for treatment effects, uplift, and counterfactual prediction

• Design experiments, analyze randomized controlled trials, and evaluate results

• Develop constrained optimization models for promotion and incentive allocation

• Read research papers, prototype ideas, and turn the ones that work into real systems

• You're strong in mathematics, probability, and statistics and enjoy reasoning about problems formally.

• You have strong quantitative modeling fundamentals, whether your background is in machine learning, economics, finance, operations research, statistics, or another technical field.

• You understand causal and experimental reasoning and are comfortable thinking about RCTs, counterfactuals, treatment effects, and confounding.

• You're interested in optimization and decision making, such as allocating limited resources, optimizing under constraints, or choosing actions under uncertainty.

• You know how to write good Python code from projects, research, coursework, or similar experience.

• You're comfortable with modern AI, including neural networks, representation learning, transformers, and LLMs, or have the technical background and interest to learn them quickly. Experience with PyTorch/JAX is a strong plus.

• You can make sense of research by reading papers, understanding the key ideas, questioning assumptions, and turning them into working code.

• You work well with others, are genuinely curious, and enjoy learning new ideas and technologies.

• You're friends with AI assistants and use them regularly for coding, research, and writing.

• You can commit to 6-12 months working with us in Amsterdam (minimum 3 days in the office weekly). We'll work with your academic schedule.

Experience with causal inference, econometrics, empirical economics, operations research, quantitative finance, uplift modeling, treatment-effect estimation, or mathematical optimization is a plus. This could include methods such as S/T/X-learners, causal forests, causal DAGs, potential outcomes, linear or integer optimization, Lagrangian/dual methods, stochastic optimization, or related techniques.

Prior experience with promotions, pricing, ads, recommendations, logistics, marketplaces, or other allocation and decision systems is a strong bonus, but not required.