Summer Intern, PhD AI/ML Scientist

Why This Role Matters

The Washington Post is on a mission to reinvent how journalism is created, personalized, and delivered. Our AI/ML organization powers breakthrough experiences across Generative AI, Personalized News & Recommendations, Revenue Science & Reinforcement Learning, and Core Search & Ranking systems. As a PhD intern, you will work alongside scientists, engineers, and product partners to solve some of the most challenging problems at the intersection of machine learning, information retrieval, generative LLM systems, and digital news consumption. The following teams are available for internship:

• Generative AI (Ask The Post, Research Mode, RAG Systems): Build next-generation conversational AI products, retrieval-augmented generation pipelines, reasoning engines, hallucination-safe summarization, and evaluation frameworks that power Ask The Post, personalized podcasts, and research-assisted news exploration.
• Personalization & Recommendations: Design and train large-scale ranking systems, two-tower models, graph-based recommenders, embeddings, and experimentation pipelines that power personalized modules such as For You, home-page ranking, article recommendations, and real-time user modeling.
• Revenue Science (Metering & Paywall Optimization, Ads Relevance, RL Systems): Apply causal inference, reinforcement learning, optimization, and behavioral modeling to maximize subscription growth and advertising revenue while improving long-term reader value.

How You'll Support the Mission

As a PhD intern, you will:

• Lead a full-cycle applied research project from problem definition to experiment to production-ready prototype.
• Conduct original research in machine learning, generative AI, NLP, recommender systems, RL, causal inference, or information retrieval.
• Work with large-scale behavioral, content, and interaction datasets to uncover insights and build intelligent systems.
• Develop novel algorithms for:
  • Retrieval-augmented generation and grounding
  • Multi-turn and agentic search
  • Personalization and user modeling
  • Reinforcement learning for metering and pricing
  • Large-scale ranking and embeddings
• Design and run A/B tests, offline evaluations, and model-driven product experiments.
• Collaborate closely with engineering, design, and product partners across the organization.
• Publish internal research and, where appropriate, externalize work at top conferences.
• This is an opportunity to conduct high-impact applied research with immediate real-world influence.

The Skills and Experience You Bring

Required Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, NLP, Information Retrieval, or a related field.
• Strong research background in one or more areas: LLMs/GenAI, NLP, IR/Search, Recommender Systems, RL, Causal Inference, Optimization, Graph ML, or Representation Learning.
• Proficiency in at least one ML-oriented programming language or framework: Python, PyTorch, TensorFlow
• Experience handling large-scale datasets, distributed computing, or experimentation platforms.
• Ability to independently define research questions, design experiments, and synthesize findings.
• Excellent written and verbal communication skills, especially in explaining complex technical ideas to non-technical audiences.

Preferred Qualifications

• Publications in machine learning, AI, NLP, IR, recommender systems, or related areas.
• Experience building or evaluating Conversational Systems, RAG systems, Fine-Tuning LLMs, Multiarm Bandits, and Reinforcement Learning.
• Experience with cloud computing platforms (e.g., AWS), big-data technologies (e.g., Spark, Beam, BigQuery), and/or real-time serving systems.
• Strong interest in applying ML to real-world solutions that power personalization and audience engagement with journalism.