Join the future of news

We’re on a mission to deliver riveting storytelling for all of America. At The Washington Post, you’ll help reinvent news. Our work is driven by a deep investigative spirit and enhanced by innovation to bring audiences closer to the stories that matter most.

About Our Team

The Washington Post is powered by the passion and talent of our people. It takes all of us to reinvent news. Beyond our award-winning News and Opinions teams, we work across many departments, including Brand & Events, Communications, Customer Care, Engineering & Product, Finance, Human Resources, Legal, Marketing & Advertising, Print Operations, and Sales.

Why This Role Matters

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.

What Motivates You

• You value world-class journalism and want to support it through practical AI/ML solutions.

• You enjoy building models that improve reader experience and business outcomes.

• You are interested in personalization, recommender systems, ranking, generative AI, and experimentation.

• You collaborate well, communicate clearly, and respond positively to feedback.

• You are eager to grow by learning and applying modern AI/ML techniques.

How You'll Support the Mission

• 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.

The Skills and Experience You Bring

• 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.

Preferred Qualifications

• 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.

Collaboration makes us stronger. That’s why our offices are designed with open layouts, modern technology, and easy access to transportation. With certain exceptions for newsgathering and business travel, we work on-site five days a week.

Compensation and Benefits

Wherever you are in your life or career, The Washington Post offers comprehensive and inclusive benefits for every step of your journey:

• Competitive medical, dental and vision coverage

• Company-paid pension and 401(k) match

• Three weeks of vacation and up to three weeks of paid sick leave

• Nine paid holidays and two personal days

• 20 weeks paid parental leave for any new parent

• Robust mental health resources

• Backup care and caregiver concierge services

• Gender affirming services

• Pet insurance

• Free Post digital subscription

• Leadership and career development programs

Benefits may vary based on the job, full-time or part-time schedule, location, and collectively bargained status.

The salary range for this position is:

The actual salary within this range will depend on individual skills, experience, and qualifications as they relate to specific job requirements. This position may be eligible for a bonus or incentive program, and a member of the Talent Acquisition team will discuss bonus payment terms and conditions during the interview process.