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Key Responsibilities

• Rapid Prototyping and Production Deployment: Lead the prototyping, iteration, and deployment of sophisticated reader experiences powered by LLMs and content embeddings. This work will be conducted collaboratively with fellow engineers and a diverse group of cross-functional peers (including product managers and designers) from early concept stages through to full production release.
• Full-Stack Development: Execute development across the entire application stack, utilizing robust Node.js/TypeScript for the backend services, React for dynamic and responsive front ends, and integrating advanced A.I. capabilities seamlessly across both layers.
• Net-New A.I. Feature Integration: Build entirely new A.I. features and integrate them carefully into existing systems and APIs. A key component of this will be exercising sound judgment in determining when to build a component from scratch versus cleanly wiring together and leveraging established infrastructure.
• Prompt Engineering as a Core Discipline: Write, rigorously test, and refine complex prompts and retrieval pipelines. You are expected to treat prompt engineering not as a minor task but as a first-class engineering discipline, vital for the success of A.I.-powered features.
• Data-Driven Iteration: Thoroughly instrument all new features with comprehensive evaluations, crucial engagement signals, and detailed performance monitoring. Actively utilize this data to drive continuous improvement and optimization of the features.
• Cross-Functional Collaboration: Work in close partnership with product managers, UX designers, journalists, and senior engineers to effectively translate complex editorial and journalistic goals into viable, high-quality working products.
• Shaping Best Practices: Actively contribute to developing and maintaining shared organizational practices around feature evaluation, responsible A.I. usage, and defining what "good " product development looks like within an organization where editorial judgment and institutional independence are paramount and non-negotiable.

Minimum Requirements

• Experience: 5-8 years of demonstrated, hands-on software engineering experience with a clear and established ability to ship impactful, consumer-facing features.
• LLM Production Experience: Essential, hands-on experience building with Large Language Models within a real product context—going significantly beyond mere side projects or proof-of-concepts.
• Full-Stack Command: Strong fundamental knowledge of the full web stack: demonstrated proficiency with Node.js or TypeScript on the backend and deep experience with React on the front end.
• Technical Adaptability: Comfort and confidence navigating and contributing to unfamiliar or complex codebases and effectively working within established, often large-scale, backend infrastructure.
• Product Instinct: A strong, innate product instinct; the ability to consistently think first and foremost about the end-user experience, not simply the technical implementation details.

Preferred Qualifications

• System Integration Versatility: Experience working across both "greenfield " product development (building entirely new systems) and the challenging but rewarding integration with legacy systems or third-party APIs.
• Python Proficiency: Additional backend experience in Python, particularly for building robust data pipelines, machine learning model integrations, or other data-intensive tasks.
• Search and Personalization Expertise: Specific experience with advanced concepts such as semantic search, embedding-based retrieval systems, or sophisticated content personalization platforms.
• Agentic Software Development: Experience building "agentic " software—systems where models are designed to plan, take actions, or operate across multiple, sequential steps; in this area, the complexity of the systems you've built matters more than mere professional tenure.
• Evaluation and Testing Frameworks: Familiarity with evaluation frameworks tailored for A.I. features or significant experience with A/B testing methodologies in the context of machine learning applications.
• Trust-Sensitive Product Background: A professional background working in complex, highly trust-sensitive product environments where deep editorial or institutional judgment fundamentally shapes the product development and release process.
• Curiosity and Passion: A strong curiosity about the field of journalism and a genuine interest in the evolving future of how people consume and interact with information.