Senior Machine Learning Engineer

washpost; Washington (District of Columbia); On-site; USD 140,000 - 190,000; Full time

Job summary

The Washington Post seeks a senior machine learning engineer to design and scale production AI/ML systems powering journalism, reader experiences, and business goals. You will work on AI/ML platforms, search, retrieval, ranking, and GenAI-powered features in a high-performance environment.

You will partner with Applied Research Scientists to translate research into scalable production systems, focusing on low-latency inference, model serving, and observability.

Qualifications

• Bachelor's degree or higher in Computer Science, Computer Engineering, Machine Learning, or related field, or equivalent practical experience.
• 5+ years of experience in machine learning engineering, software engineering, or building large-scale production ML systems.
• Strong programming skills in Python and experience with a production language such as C++, Java, Go, Scala, or Rust.
• Strong knowledge of software engineering fundamentals, including data structures, algorithms, APIs, distributed systems, testing, and system design.
• Hands-on experience with PyTorch or another modern machine learning framework and deploying deep learning models into production.
• Experience designing and operating end-to-end ML systems, including training, deployment, inference, monitoring, and continuous improvement.
• Strong understanding of low-latency online inference, including CPU/GPU serving, batching, caching, memory utilization, and performance optimization.
• Experience building search, retrieval, ranking, recommendation, GenAI, or related machine learning systems.
• Experience with Kubernetes, Docker, CI/CD, AWS, GCP, Spark, Beam, Kafka, or similar cloud and distributed systems technologies.
• Experience with IaC, such as CloudFormation, CDK, or Terraform.
• Experience mentoring engineers, leading technical projects, and influencing AI/ML platform and architecture roadmaps.

Responsibilities

• Design, build, deploy, and operate production ML systems across generative AI, search, Revenue Science and Personalization.
• Partner with Applied Research Scientists to translate research models and prototypes into scalable, reliable, and maintainable production systems.
• Design and build high-throughput, low-latency ranking and retrieval services supporting real-time personalization, recommendation, search, and intelligent discovery.
• Build and evolve reusable AI/ML platform capabilities for model training, feature generation, experimentation, deployment, inference, model versioning, and monitoring.
• Design and optimize CPU and GPU inference systems for LLMs, embedding models, rerankers, ranking models, and other deep learning workloads.
• Develop scalable online and batch ML architectures for feature pipelines, embedding generation, candidate retrieval, ranking, and model inference.
• Optimize model serving for latency, throughput, reliability, and cost using batching, caching, quantization, distillation, and hardware-aware optimization.
• Build and operate production systems for semantic and hybrid search, vector retrieval, retrieval-augmented generation, recommendation, and agentic AI workflows.
• Establish strong ML observability across model quality, data and feature health, latency, throughput, failures, and resource utilization.
• Provide technical leadership through system design, architecture reviews, mentoring, and shaping the technical roadmap for AI/ML platforms and production systems.

Skills

Python

ML engineering

Distributed systems

PyTorch

End-to-end ML systems

Low-latency inference

Kubernetes

Docker

Cloud platforms

System design

Mentoring engineers

Education

Bachelor's degree or higher in Computer Science/Engineering or related field

Tools

Kubernetes

Docker

CI/CD

AWS

GCP

Spark

Beam

Kafka

Terraform

CloudFormation

Job description

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

As a senior machine learning engineer, you will design, build, and scale production AI/ML systems that power the next generation of journalism. This role will focus on AI/ML platforms, Ask The Post, search, retrieval, ranking, recommendation, and generative AI-powered reader experiences.

You will work at the intersection of machine learning and software engineering, translating advanced AI/ML research into scalable, low-latency, reliable production systems that serve The Post's journalism, readers, and business goals.

What Motivates You

• You value world-class journalism and want to build technology that strengthens that mission.
• You enjoy turning promising machine learning research and prototypes into scalable production systems.
• You are excited by AI/ML platforms, search, retrieval, ranking, recommendation, generative AI, and high-performance inference.
• You are passionate about performance, reliability, scalability, observability, maintainability, and infrastructure efficiency.
• You communicate clearly, collaborate well, and thrive in a feedback-driven engineering environment.

How You'll Support The Mission

• Design, build, deploy, and operate production ML systems across generative AI, search, Revenue Science and Personalization.
• Partner with Applied Research Scientists to translate research models and prototypes into scalable, reliable, and maintainable production systems.
• Design and build high-throughput, low-latency ranking and retrieval services supporting real-time personalization, recommendation, search, and intelligent discovery.
• Build and evolve reusable AI/ML platform capabilities for model training, feature generation, experimentation, deployment, inference, model versioning, and monitoring.
• Design and optimize CPU and GPU inference systems for LLMs, embedding models, rerankers, ranking models, and other deep learning workloads.
• Develop scalable online and batch ML architectures for feature pipelines, embedding generation, candidate retrieval, ranking, and model inference.
• Optimize model serving for latency, throughput, reliability, and cost using techniques such as batching, caching, quantization, distillation, and hardware-aware optimization.
• Build and operate production systems for semantic and hybrid search, vector retrieval, retrieval-augmented generation, recommendation, and agentic AI workflows.
• Establish strong ML observability across model quality, data and feature health, latency, throughput, failures, and resource utilization.
• Provide technical leadership through system design, architecture reviews, mentoring, and shaping the technical roadmap for AI/ML platforms and production systems.

The Skills and Experience You Bring

• Bachelor's degree or greater in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
• 5+ years of experience in machine learning engineering, software engineering, or building large-scale production ML systems.
• Strong programming skills in Python and experience with a production language such as C++, Java, Go, Scala, or Rust.
• Strong knowledge of software engineering fundamentals, including data structures, algorithms, APIs, distributed systems, testing, and system design.
• Hands-on experience with PyTorch or another modern machine learning framework and deploying deep learning models into production.
• Experience designing and operating end-to-end ML systems, including training, deployment, inference, monitoring, and continuous improvement.
• Strong understanding of low-latency online inference, including CPU/GPU serving, batching, caching, memory utilization, and performance optimization.
• Experience building search, retrieval, ranking, recommendation, GenAI, or related machine learning systems.
• Experience with Kubernetes, Docker, CI/CD, AWS, GCP, Spark, Beam, Kafka, or similar cloud and distributed systems technologies.
• Experience with IaC, such as CloudFormation, CDK, or Terraform.
• Experience mentoring engineers, leading technical projects, and influencing AI/ML platform and architecture roadmaps.

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