Senior Software Engineer

• Author, test, review, and optimize production-quality code, following best practices for IaC, version control, and continuous delivery with minimal oversight
• Be a subject matter expert in one or more AI Systems production systems, independently shipping high- quality features and helping others understand complex technical domains
• Design components and systems architecture, driving technical decisions that create functional-level impact
• Integrate ML models and foundation models into production applications with appropriate guardrails, monitoring, and evaluation
• Solve business problems with simple and straightforward solutions, proactively defining problems and driving clarity for the team
• Drive operational excellence - building highly available, low-latency, and efficient software while thinking long-term about code health, monitoring, alerting, and documentation
• Partner with editorial, product, and ML stakeholders to translate requirements into reliable AI features
• Communicate effectively across audiences - technical documentation, code reviews, design reviews, and interactions with stakeholders and adjacent teams
• Embrace failure as a learning opportunity - use research and experimentation to choose solutions that meet company goals, moving autonomously from proof-of-concept to production

• 5+ years of professional software engineering experience with a Bachelor's degree in Computer Science, Information Technology, or a related technical field (or 3+ years with a Master's degree)
• Strong backend engineering experience with data-intensive applications at web scale
• Proficiency in Python and at least one of Go, Java, or C++
• Solid foundation in relational databases (Postgres or equivalent), NoSQL databases (DynamoDB or equivalent), and infrastructure as code (Terraform or equivalent)
• Demonstrated ability to design, build, and ship highly available, low-latency systems
• Track record of independently driving complex technical projects from concept to production - knowing when to own decisions, when to collaborate for alignment, and when to escalate appropriately The

Nice to Haves

• Experience integrating LLMs or foundation models into production applications
• Familiarity with prompt engineering, evaluation frameworks, or LLM observability
• Experience with classification, content understanding, or information retrieval systems
• Understanding of experimentation frameworks and A/B testing methodologies
• Prior experience working on a machine learning or AI team
• Background in media, publishing, or news organizations

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