Senior Machine Learning Engineer

The Washington Post•Washington, DC

5d•$119,700 - $199,300•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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 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.