Role Description We are looking for Computer Science engineers with PhD diploma to contribute to advanced AI model evaluation projects focused on reasoning-heavy computer science challenges. In this role, you will help assess and improve frontier AI systems by creating complex conceptual problems and evaluating the quality of AI-generated reasoning across multiple CS domains. - Work on topics such as algorithms, systems design, databases, cybersecurity, computer networks, distributed systems, and other advanced undergraduate to PhD-level computer science areas. - The ideal candidate combines strong theoretical foundations with the ability to critically analyze technical reasoning, edge cases, assumptions, and solution correctness. CONTRACT: Contractor assignment (12-week project), paid per completed task COMMITMENT: 20-40 hours/week with minimum 4 hours PST overlap LOCATION: Remote — Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, Vietnam PROCESS: 1 Online Test + 1 Interview NOTE: Accepted candidates need to go through criminal background check before starting work. Responsibilities - Design advanced computer science problems focused on reasoning and conceptual understanding - Create structured reference solutions with clear logic and technical explanations - Evaluate AI-generated answers for correctness, reasoning quality, completeness, and clarity - Identify edge cases, logical flaws, and weak reasoning patterns in model outputs - Review complex topics across algorithms, systems, databases, networking, security, and theory - Contribute to benchmark and evaluation quality improvements - Provide detailed feedback to support AI model training and evaluation - Collaborate with researchers and reviewers on problem refinement - Maintain high-quality standards and consistent evaluation methodology Qualifications - PhD in Computer Science or a closely related field (completed or in progress) - Strong theoretical and problem-solving background across advanced CS domains - Ability to design conceptual and reasoning-heavy technical problems - Strong understanding of algorithms, systems, databases, networking, or cybersecurity - Excellent written English communication skills - Strong analytical skills and attention to technical detail - Ability to critically evaluate reasoning, assumptions, and solution validity

• Contribute to and lead delivery of CX automation and AI initiatives, from scoping and design through to production and ongoing iteration. • Build and maintain AI agents and automated workflows that improve specialist effectiveness, reduce manual handling, and increase consistency across CX operations. • Deploy AI capabilities responsibly: design for failure, ensure appropriate human oversight is built in, and maintain the logs and auditability expected in a customer-facing support environment. • Stay across developments in AI and automation tooling, surface well-reasoned recommendations for your manager and the CX leadership team to evaluate. • Own the technical delivery of CX tooling: the integrations, applications, and automated workflows that connect the platforms CX teams depend on every day. • Build and maintain integrations across the CX technology stack, including Zendesk and its application framework, Remote's internal platform, REST APIs, webhooks, and emerging integration standards such as MCP, ensuring data flows correctly, triggers fire reliably, and tooling holds up under operational load. • Manage complex, multi-phase delivery: own dependencies, communicate proactively, and bring initiatives across the line without needing to be chased. • Use operational data and CX performance metrics to inform priorities and validate what is working. Build visibility into the impact of automation for CX leadership. • Work closely with CX operations and front-line teams to understand workflow pain points, validate solutions before they ship, and support adoption after they do. • Partner with engineering, product, and data teams across Remote to align on architecture and escalate when a problem needs more than automation to solve. • Communicate clearly and regularly with stakeholders at all levels: operational teams, CX leadership, and cross-functional partners.

• Develop ready-made skills — reusable components for common AI tasks (text analysis, classification, RAG, agentic workflows, etc.) • Optimize prompts across different models and scenarios; conduct A/B testing and measure output quality • Design RAG integrations and agentic pipelines — from architecture to implementation • Work with integrations — connecting external APIs, vector databases, knowledge sources • Test and document skills — create usage examples, handle edge cases, establish best practices • Analyze requirements and turn them into scalable components • Monitor skill performance and iterate based on feedback • Stay on top of AI trends and add new capabilities to the platform

• Develop complete examples and applications in Quadcode.ai IDE: multimodal generations, RAG scenarios, agentic pipelines • Create and fine-tune **complex, reproducible prompts** for different models and tasks • Build a portfolio of projects with high visual appeal and clear structure (categories, tags, versions, source code) • Prepare case descriptions, video demonstrations, and presentations (problem → implementation → results) • Experiment with new platform capabilities and AI trends • Work closely with the engineering team, provide product feedback • Represent the project in public formats where appropriate: demos, articles, social media