Senior Applied Machine Learning Scientist

The Washington Post•Washington, DC

2d•$131,500 - $219,100•Onsite

About The Position

The Washington Post is looking for a Senior Applied Machine Learning Scientist to lead the development of advanced AI/ML systems that power the next generation of journalism. This role will focus on Ask The Post, AI-powered search, retrieval, ranking, content understanding, and generative AI-powered reader experiences. You will work at the intersection of generative AI, information retrieval, NLP, ranking, recommender systems, and experimentation to build intelligent systems that serve The Post’s journalism, readers, and business goals.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Statistics, Machine Learning, or a related technical field.
4+ years of experience in applied machine learning, AI, data science, information retrieval, NLP, recommender systems, or a related field.
Strong experience with Python and at least one ML framework such as PyTorch, TensorFlow, or JAX.
Experience designing, evaluating, and deploying ML models using large-scale datasets.
Strong foundation in machine learning, statistical analysis, experimental design, and model evaluation.
Experience with search, ranking, retrieval, NLP, GenAI, or recommendation systems.

Nice To Haves

Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, NLP, or a related field.
Deep familiarity with modern ML architectures, including transformer-based models, embedding models, two-tower architectures, LLMs, VLMs, and their applications in large-scale search, retrieval, ranking, and recommender systems.
Hands-on experience building production systems for semantic search, retrieval-augmented generation, question answering, ranking, content understanding, recommendation, or GenAI-powered products.
Experience with AWS, GCP, Spark, Beam, BigQuery, or similar cloud and big-data technologies.
Experience with LLM/VLM evaluation, model calibration, uncertainty estimation, interpretability, responsible AI, or production model optimization.
Experience with deep learning, reinforcement learning, multi-armed bandits, causal inference, or learning-to-rank methods.
Publications, patents, open-source contributions, or technical talks in AI/ML, NLP, information retrieval, recommender systems, GenAI, or related areas.
Experience mentoring scientists or engineers and influencing technical roadmaps.

Responsibilities

Lead applied research and development for AI/ML systems across generative AI, search, retrieval, ranking, content understanding, and recommendation.
Formulate ambiguous product and business challenges as scientific problems with clear metrics, experiments, and success criteria.
Design, train, evaluate, and deploy advanced AI/ML models, including fine-tuned mid-range LLMs, dense and sparse embedding systems, VLMs, learning-to-rank models, agentic workflows, and efficient model-serving approaches for conversational search systems.
Build and improve AI-powered experiences such as Ask The Post, semantic search, content understanding, question answering, ranking, and intelligent discovery.
Design rigorous offline and online evaluations, including relevance evaluation, ranking metrics, retrieval quality, A/B testing, and causal analysis.
Develop scalable and efficient production ML systems with attention to latency, reliability, cost, monitoring, and maintainability.
Analyze large-scale behavioral, content, search, and interaction data to guide model and product improvements.
Collaborate with scientists, engineers, data teams, product managers, editors, and other business stakeholders to deliver impactful AI/ML solutions.
Provide technical leadership by shaping roadmaps, mentoring junior scientists and engineers, and promoting scientific rigor.
Communicate technical approaches, tradeoffs, results, and business impact to technical and non-technical audiences.