Software Engineer I, AI Specialist

With deep domain expertise, advanced technical capabilities, and a proven track record of successful collaborations, the AI Enablement & Machine Learning team at CNN is accelerating our digital transformation through strategic applications of machine learning and AI technologies.

The AI Systems & Enablement group within this team builds and operates AI-powered applications for CNN's audiences and internal teams, and drives AI adoption across the organization through tool governance, training, and enablement programs. Current products include article summaries, content and product classification, weather summaries, and brand safety classification. The team also leads CNN's AI enablement efforts — including AI Practices Guild leadership, Claude Code rollout, foundation model governance, and productivity measurement.

Our vision is that CNN delivers AI-powered features that enhance user experience and internal productivity with systems that are reliable, accurate, and trustworthy — meeting the high standards our audiences expect from a news organization — while equipping the broader organization with the tools, governance, and skills to work effectively with AI.

Your New Role...

The Applied AI Specialist is a non-traditional Business Analyst role within the AI Systems workstream, reporting to a Software Engineering Manager. Rather than traditional requirements gathering and process documentation, this role focuses on the critical intersection of AI capabilities and CNN's content, editorial standards, and product experiences. You will be hands-on with our AI-powered features — designing and refining prompts, building evaluation frameworks, structuring human feedback workflows, and working directly with engineering and editorial partners to ensure AI outputs meet CNN's standards for accuracy and trustworthiness.

This role is ideal for someone with deep knowledge of content operations, editorial standards, or content classification — combined with a strong understanding of how AI and large language models work. You will work alongside software engineers who handle code, infrastructure, and optimization. Your focus is on the quality, accuracy, and appropriateness of what our AI features produce.

Key areas you will work on

Prompt Engineering: Design, build, iterate on, and maintain prompts for production AI features including article summaries, content classification, brand safety classification, and new features as they launch. Develop and document prompting patterns and templates that can be reused across projects.

Evaluation & Quality: Design evaluation criteria and workflows for AI features. Define what "good" looks like for each use case, build evaluation datasets, run quality assessments, and track accuracy over time. Identify when and where AI outputs need human review.

Human-in-the-Loop Workflows: Design and execute workflows for human feedback on AI outputs. Determine when human intervention is necessary, structure how that feedback is systematically captured, and work with ML and engineering partners to feed it back into prompt and model improvement cycles.

Cross-Functional Collaboration: Partner closely with engineers on your team who own the technical implementation of AI features, as well as editorial stakeholders on assigned projects. Help translate AI capabilities and limitations into terms that inform feature decisions and editorial guidelines. Communicate what's feasible, what the tradeoffs are, and what's needed to improve quality for specific use cases.

Your Role Accountabilities...

• Own the prompt engineering lifecycle for assigned AI features — from initial design through iteration, testing, and production maintenance

• Build and maintain evaluation frameworks that measure AI output quality against CNN's editorial and accuracy standards

• Design and run human review workflows, ensuring feedback is structured and actionable for engineering and ML partners

• Partner closely with engineers on your team who handle code, architecture, caching, optimization, and deployment — providing the content expertise, evaluation data, and quality guidance that shapes what the system produces

• Work directly with editorial partners on assigned projects as a primary point of contact for AI output quality, helping them understand what AI can and can't do and what is needed to improve results

• Communicate the nuances of working with LLMs — including their stochastic nature, cost considerations, and the importance of evaluation — to engineering and editorial teammates in clear, practical terms

• Contribute to documentation of prompting patterns, evaluation approaches, and quality standards that the team can build on over time

• Stay current with developments in prompt engineering, LLM capabilities, and AI evaluation practices

Here Is the Approach We Value:

• Demonstrate meticulous attention to detail and a deep sense of responsibility for the accuracy and quality of AI-generated content

• Bring strong domain knowledge of content, editorial processes, or classification systems — and apply that expertise to make AI features better

• Embrace experimentation and iteration — prompt engineering and evaluation are evolving disciplines, and the best results come from systematic testing and refinement

• Communicate complex AI concepts in simple, practical terms to partners across editorial and engineering

• Take ownership of quality for the features you support, proactively identifying issues and proposing improvements

• Collaborate effectively across functions, understanding that great AI features require tight partnership between content experts, engineers, and editorial teams

• Approach ambiguity with curiosity rather than hesitation — best practices in applied AI are still being established, and you will help define them

Qualifications & Experience...

• 2–4 years of experience in content operations, content classification, editorial workflows, taxonomy management, or a related field

• Demonstrated experience with prompt engineering for large language models in a professional or academic setting

• Experience designing evaluation criteria or quality assessment workflows for content or AI outputs

• Strong understanding of how LLMs work — including their capabilities, limitations, stochastic behavior, and cost implications

• Excellent written and verbal communication skills, with the ability to explain technical AI concepts to non-technical audiences

• Detail-oriented with strong analytical skills and comfort working with evaluation data and quality metrics

• A collaborative mindset and comfort working closely with software engineers and editorial stakeholders

• Comfort navigating ambiguity in a fast-evolving space — applied AI best practices are still being established

The Nice to Haves...

• Degree or certification in AI, machine learning, data science, or a related field

• Experience with content classification, taxonomy, or content management systems

• Experience in a news, media, or content-heavy environment where accuracy and trust are paramount

• Familiarity with AI evaluation frameworks, annotation tools, or human-in-the-loop methodologies

• Experience working with cross-functional teams that include editorial and engineering stakeholders

• Understanding of information retrieval concepts and how they apply to AI-powered features

• Basic scripting skills (e.g., Python) for data analysis or automation of evaluation workflows

• Experience with requirements gathering, process documentation, or business analysis in a technical environment