Full-Time
Senior Applied Machine Learning Scientist
Posted on 8/19/2026
Washpost
Compensation Overview
$131.5k - $219.1k/yr
+ Bonus or incentive program
Washington, DC, USA
In Person
On-site five days per week is required, except for certain newsgathering and business travel.
Bachelor's, Master's, PhD
Category
 AI & Machine Learning (1) |
|---|
Required Skills
LLM
Python
TensorFlow
Neural Networks
PyTorch
BigQuery
Apache Spark
Machine Learning
A/B Testing
AWS
Data Analysis
Reinforcement Learning
Google Cloud Platform
Requirements
- A bachelor's degree in Computer Science, Mathematics, Statistics, Machine Learning, or a related technical field is required.
- At least 4 years of experience in applied machine learning, artificial intelligence, data science, information retrieval, natural language processing, recommender systems, or a related field is required.
- Strong experience with Python and at least one machine learning framework such as PyTorch, TensorFlow, or JAX is required.
- Experience designing, evaluating, and deploying machine learning models using large-scale datasets is required.
- A strong foundation in machine learning, statistical analysis, experimental design, and model evaluation is required.
- Experience with search, ranking, retrieval, natural language processing, generative artificial intelligence, or recommendation systems is required.
Responsibilities
- Lead applied research and development for artificial intelligence and machine learning systems across generative artificial intelligence, 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 artificial intelligence and machine learning models, including fine-tuned mid-range large language models, dense and sparse embedding systems, vision-language models, learning-to-rank models, agentic workflows, and efficient model-serving approaches for conversational search systems.
- Build and improve artificial-intelligence-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 machine learning 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 artificial intelligence and machine learning 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.
Desired Qualifications
- A master's or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, Natural Language Processing, or a related field.
- Deep familiarity with modern machine learning architectures, including transformer-based models, embedding models, two-tower architectures, large language models, and vision-language models, 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 generative-artificial-intelligence-powered products.
- Experience with AWS, Google Cloud Platform, Apache Spark, Apache Beam, BigQuery, or similar cloud and big-data technologies.
- Experience with large-language-model and vision-language-model evaluation, model calibration, uncertainty estimation, interpretability, responsible artificial intelligence, 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 artificial intelligence and machine learning, natural language processing, information retrieval, recommender systems, generative artificial intelligence, or related areas.
- Experience mentoring scientists or engineers and influencing technical roadmaps.