emberos

Full-stack Engineer

• Develop and extend our Brand Knowledge Graph in Neo4j: modeling how changes to one node propagate and affect AI visibility and recommendations elsewhere
• Build predictive optimization systems that model how specific content and strategy changes impact AI visibility outcomes
• Design measurement and feedback loops that connect changes to outcomes and track predicted vs. actual lift over time
• Implement scoring logic including Share-of-Prompt, accuracy measurement, sentiment analysis, and competitor mention detection
• Analyze how optimizations at scale affect long-term LLM behavior and ecosystem dynamics
• Build and maintain integrations with LLM platforms including ChatGPT, Claude, Grok, Perplexity, DeepSeek, and others as they emerge
• Design orchestration systems that minimize unnecessary LLM calls: managing cost, latency, and quality tradeoffs intelligently
• Develop agent-based analysis workflows that evaluate multiple optimization scenarios in parallel and forecast impact
• Compare and stress-test multiple optimization strategies simultaneously to surface the most effective approaches
• Build and ship full-stack features across frontend, backend, and data layers: from idea to production
• Develop high-quality frontend interfaces in React and TypeScript that translate complex graph and model outputs into actionable insights for users
• Design and optimize backend systems for performance, security, and scalability
• Build workflow and tracking infrastructure recording what changed, why it changed, and the outcome -- integrating with Jira, Slack, HubSpot, and email
• Lay foundations for AI-native commerce experiences where merchants can transact directly inside AI chat
• Work closely with design and product to translate ideas into polished, production-ready experiences
• Participate actively in client calls, demos, and technical conversations -- explaining systems clearly to non-technical enterprise stakeholders
• Own QA processes and fix pack delivery: building test coverage, triaging bugs, and maintaining data integrity across the platform
• Pull insights from complex datasets and translate them into findings that clients and internal teams can act on
• Document architecture, decisions, and systems to support a growing team and future CTO onboarding

Full-Stack Engineer, AI Product, Client Solutions