Your New Role...
As a Software Engineer II on AI Systems, you will design, build, and operate AI-powered applications and the platform capabilities that support them. You will partner with ML engineers, editorial and product stakeholders, and platform engineers to ship features that improve CNN's user experience and internal productivity.
Key challenges you will tackle:
LLM-Based Content Understanding: Build the in-house, LLM-based content understanding capability that replaces a vended classification product — improving quality, expanding adoption to new business domains across CNN, and powering downstream use cases including ad targeting, content routing, and brand safety.
Consumer-Facing AI Features: Ship and iterate on AI-powered features that reach millions of users — article summaries, weather summaries, and the next set of audience-facing experiences — with the accuracy and trustworthiness CNN's audiences expect.
AI Platform Maturation: Grow CNN's AI platform capabilities — prompt management, evaluation, caching, versioning, deployment, and tenant management — so that AI features across CNN can be built, tested, and operated reliably and efficiently.
Internal AI Tooling: Build AI-assisted workflows for editorial, product, design, engineering, and business teams that drive measurable productivity gains.
What You'll Do
- 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
- Provide technical input during design of 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
The Essentials - 3+ years of professional software engineering experience with a Bachelor's degree in Computer Science, Information Technology, or a related technical field (or 2+ 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
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
