Applied AI Engineer, NewsBreak

• Education Background: Bachelor's/Master's degree (recently completed or completing within the next 6 months) in Computer Science, Artificial Intelligence, Data Science, or related fields
• Work Experience: No professional work experience required; relevant internships, research, coursework, or personal projects involving LLMs, agents, or ML are a plus
• Exposure to building or experimenting with agentic systems (e.g., through coursework, hackathons, internships, or personal projects)
• Solid software-engineering fundamentals: ability to write clean, testable code; familiarity with asynchronous/concurrent programming and typed/data-modeled code
• Core Technical Stack: Foundational understanding of LLMs, RAG, tool-use, and agent orchestration concepts (including MCP-style tool ecosystems), gained through coursework, projects, or self-study
• Some hands-on experience with structured/schema-validated LLM outputs and modern model APIs (e
• , through class projects or internships)
• Familiarity with REST/API integration; exposure to deployment tools such as Docker or Kubernetes is a plus
• Solid grounding in machine learning and AI fundamentals
• Strong analytical skills and eagerness to learn complex problem-solving in a production environment

Desirable

• Are you a recent graduate excited to apply LLMs and Agent technology to real advertising products from day one?
• Join our advertising team to help build an AI-driven intelligent account hosting and optimization platform
• You'll work alongside senior engineers to develop AI advertising expert systems (AI Agents) that diagnose accounts and safely auto-tune delivery in a closed optimization loop
• Your work will directly lower the delivery barrier for global advertisers on our platform
• While ensuring the health and compliance of the account ecosystem, your contributions will help maximize advertisers' willingness to spend and platform revenue
• Contribute to the In-house AI Account Manager System: Support the design and implementation of components of the "AI Account Manager," including automated hosting, optimization scheduling, and monitoring across concurrently running advertiser accounts
• Help Build Intelligent Diagnosis and Strategy Recommendation Models: Work with performance data and delivery history to help identify account health signals and delivery pain points (such as inability to scale volume, sudden drops in ROI, or budget capping); assist in using LLMs to generate actionable, auditable account-level tuning strategies
• Support Reliability and Measurable Quality: Help build and maintain observability, dry-run safeguards, and offline/online evaluation pipelines that keep optimization quality measurable and catch regressions before they reach live spend
• Collaborate and Learn: Work closely with senior engineers, the platform-side ad delivery algorithm team, and product managers, gaining exposure to how AI account management capabilities translate into commercial impact