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

Who We Are You've ordered dinner through Just Eat Takeaway. You've bought and sold on OLX. You've paid with PayU. Chances are, you've already used a Prosus company today. We are Prosus, a global technology company powering some of the world's leading lifestyle ecommerce businesses. Every day, billions of people across India, Latin America, Europe and beyond use our products and services to buy, sell, pay, connect and discover. Our AI-driven ecosystem spans 100+ companies across ecommerce, food delivery, fintech and experiences, serving billions of consumers worldwide. AI powers how we operate and innovate, with more than 60,000 active AI agents helping our teams build, scale and move faster. Through our investment team, we also back the founders and technologies shaping the future: From robotics and autonomous systems to synthetic biology and frontier AI. Guided by our five values Innovation, Results, People, Impact, and Entrepreneurship, culture is our competitive advantage. We thrive with experimentation; we build, we test, we learn, we iterate. Join us in shaping the future of technology at global scale. What you'll do: 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 Preferred qualifications & traits: 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. What we offer: Learn from the best : Work with AI engineers who've published at NeurIPS, released top models on Hugging Face, and built production systems at scale Work on hard ML problems : Combine LLMs, causal inference, experimentation, and optimization in real-world decision s