Applied Machine Learning Scientist 2

The Washington Post•Washington, DC

2d•$108,700 - $181,100•Onsite

About The Position

The Washington Post is looking for an Applied Machine Learning Scientist 2 to build AI/ML systems that help readers discover, understand, and engage with journalism. This role will focus on personalization, recommendations, ranking, user modeling, experimentation, and generative AI-powered discovery experiences. You will work with scientists, engineers, data teams, product managers, editors, and other stakeholders to turn large-scale behavioral, content, and interaction data into intelligent reader experiences.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Statistics, Machine Learning, or a related technical field.
2+ years of experience in applied machine learning, AI, data science, recommender systems, NLP, or a related field.
2+ years of professional experience with Python and at least one ML framework such as PyTorch, TensorFlow, or JAX.
Experience working with large-scale datasets and real-world ML problems.
Strong foundation in machine learning, statistical analysis, model evaluation, and experimental design.
Experience with ranking, recommendation, personalization, NLP, or GenAI applications.

Nice To Haves

Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, NLP, or a related field.
Familiarity with modern ML architectures, including transformer-based models, embedding models, two-tower architectures, LLMs, VLMs, and their applications in large-scale personalization and recommender systems.
Hands-on experience with recommender systems, learning-to-rank, personalization, user modeling, content understanding, or GenAI-powered product experiences.
Experience with AWS, GCP, Spark, Beam, BigQuery, or similar cloud and big-data technologies.
Experience with offline and online evaluation methods, including recommendation metrics, ranking metrics, A/B testing, and causal analysis.
Experience deploying ML models into production and monitoring model performance.
Exposure to LLM evaluation, prompt engineering, model calibration, responsible AI practices, or GenAI-powered content discovery.
Publications, open-source contributions, patents, or technical talks in AI/ML, NLP, recommender systems, personalization, GenAI, or related areas.

Responsibilities

Design, train, evaluate, and deploy ML models for personalization, recommendation, ranking, user modeling, and content discovery.
Build and improve systems for For You, homepage ranking, article recommendations, related content, and real-time user modeling.
Apply modern ML techniques, including embedding generation, transformer-based models, two-tower architectures, learning-to-rank models, LLM driven and GenAI-based approaches for content personalization and recommendation.
Support AI-powered reader experiences such as content understanding, question answering, intelligent discovery, and personalized content recommendations.
Work across the ML lifecycle, from problem definition and data exploration to model deployment, monitoring, and iteration.
Design offline and online evaluations, including ranking metrics, recommender-system metrics, A/B testing, and causal analysis.
Analyze large-scale behavioral, content, and interaction datasets to generate insights and improve models.
Collaborate with cross-functional teams to deliver scalable, reliable, and maintainable AI/ML solutions.
Communicate model approaches, evaluation results, tradeoffs, and impact to technical and non-technical partners.
Stay current with advances in ML, GenAI, NLP, recommender systems, ranking, and experimentation.