AI Research Intern: Causal Inference & LLMs

Prosus; Amsterdam; On-site; EUR 13,000 - 23,000; Full time

Benefits offered by this job

Learn from the best

Hard ML problems

Unique data at scale

Real infrastructure

Ship real work

Grow your career

Junior Engineer compensation

Best-in-class tools

Job summary

Prosus in Amsterdam invites you to join a driven AI team working on applying LLMs and modern machine learning to causal decision-making at scale. You will build models, run experiments, and transform research ideas into real systems while leveraging vast user data and leading ML tools.

The internship emphasizes mathematics, statistics, and quantitative reasoning, with opportunities to explore uplift modeling, treatment effects, and constrained optimization in real-world promotions and incentives.

Qualifications

• Strong foundation in mathematics, probability and statistics.
• Solid quantitative modeling fundamentals.
• Experience with causal and experimental reasoning.
• Interest in optimization and decision making.
• Proficient Python programming.

Responsibilities

• Build and experiment with LLMs, representation models, and modern ML systems for causal problems.
• Work with large-scale data to study user responses to interventions.
• Develop models for treatment effects, uplift, and counterfactual prediction.
• Design experiments, analyze randomized controlled trials, and evaluate results.
• Develop constrained optimization models for promotions and incentives.
• Read papers, prototype ideas, and turn them into production systems.

Skills

Math & stats

Quant modeling

Causal reasoning

Optimization

Python

ML fundamentals

PyTorch/JAX

Research literacy

Teamwork

AI assistants

Tools

PyTorch/JAX

Job description

Prosus in Amsterdam invites you to join a driven AI team working on applying LLMs and modern machine learning to causal decision-making at scale. You will build models, run experiments, and transform research ideas into real systems while leveraging vast user data and leading ML tools.

The internship emphasizes mathematics, statistics, and quantitative reasoning, with opportunities to explore uplift modeling, treatment effects, and constrained optimization in real-world promotions and incentives.