indeed
Senior Machine Learning Engineer
The Washington Post
Washington, USA
Full-time
Onsite
USD 119700-199300 yearly
Discovered 1 weeks ago
Machine learning engineeringPythonC++, Java, Go, Scala, or RustPyTorch or modern machine learning frameworkGenerative AISearch, retrieval, ranking, and recommendation
Free
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Ready to ScanKey skills for this role
Machine learning engineeringPythonC++, Java, Go, Scala, or Rust
Role Overview
Design, build, and scale production AI/ML systems supporting journalism, readers, and business goals.
The role spans AI/ML platforms, Ask The Post, search, retrieval, ranking, recommendation, and generative AI-powered reader experiences.
The position combines machine learning and software engineering to deliver scalable, low-latency, reliable production systems.
Key Skills for This Role
Machine learning engineeringPythonC++, Java, Go, Scala, or RustPyTorch or modern machine learning frameworkGenerative AISearch, retrieval, ranking, and recommendation
Full Job Posting
Role overview
Design, build, and scale production AI/ML systems supporting journalism, readers, and business goals.
The role spans AI/ML platforms, Ask The Post, search, retrieval, ranking, recommendation, and generative AI-powered reader experiences.
The position combines machine learning and software engineering to deliver scalable, low-latency, reliable production systems.
Responsibilities
- Design, deploy, and operate production ML systems across generative AI, search, Revenue Science, and personalization.
- Translate research models into scalable production systems with Applied Research Scientists.
- Build high-throughput, low-latency ranking and retrieval services for personalization, recommendation, search, and discovery.
- Develop reusable platform capabilities for training, feature generation, experimentation, deployment, inference, versioning, and monitoring.
- Optimize CPU and GPU inference for LLMs, embedding models, rerankers, ranking models, and other deep learning workloads.
- Develop online and batch architectures for feature pipelines, embeddings, retrieval, ranking, and inference.
- Optimize serving for latency, throughput, reliability, and cost using batching, caching, quantization, distillation, and hardware-aware techniques.
- Operate systems for semantic and hybrid search, vector retrieval, retrieval-augmented generation, recommendation, and agentic AI.
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