The Role
CubX is looking for a Senior Full Stack Product Engineer who can take a business need from concept to production.
This is a hands-on role for someone who combines strong full-stack engineering with product thinking and design judgment. You’ll work directly with business leaders and users to understand problems, design thoughtful solutions, and build polished internal applications that improve how our managed service business operates.
AI-assisted development is a core part of this role. We’re looking for someone who has successfully used AI coding tools to accelerate professional software development while maintaining ownership of architecture, quality, security, and the final product.
What You'll Do
- Lead discovery with stakeholders to understand business problems, requirements, workflows, and constraints.
- Translate complex needs into clear user journeys, product designs, and technical solutions.
- Create wireframes and prototypes and establish the visual direction for intuitive, polished interfaces.
- Design application architecture, data models, APIs, integrations, permissions, and workflows.
- Build and maintain full-stack applications using React, TypeScript, Node.js, and MongoDB.
- Partner with DevOps on Docker, CI/CD, Azure deployments, and production reliability.
- Use AI tools throughout planning, development, testing, debugging, refactoring, and documentation.
- Review and verify AI-generated code rather than treating AI output as production-ready by default.
- Own solutions through release and continue improving them based on user feedback and production behavior.
What We’re Looking For
- Proven experience taking products or significant workflows from an ambiguous business need through design, development, deployment, and ongoing improvement.
- Strong hands-on experience with TypeScript, React, Node.js, and MongoDB.
- Experience with Azure, Docker, Git, CI/CD, REST APIs, authentication, authorization, and automated testing.
- Strong product judgment and the ability to simplify complex business workflows.
- Experience creating and implementing user interfaces, not simply working from someone else’s designs.
- Strong communication skills and comfort working directly with business and technical stakeholders.
Show Us What You’ve Built
During the interview process, we’ll ask you to walk us through two examples of software you delivered using AI-assisted development.
At least one should demonstrate your involvement from stakeholder discovery and original product/design thinking through development and production release.
Be prepared to discuss:
- The business problem and how you approached it.
- Your design and architecture decisions.
- How you used AI during development.
- An AI-generated mistake or issue you identified and how you caught it.
- What ultimately shipped and how you improved it after release.
Working demonstrations, anonymized case studies, and redacted materials are welcome.
Requirements
Product and engineering requirements
- A track record of leading discovery and personally taking products or substantial workflows from an ambiguous need through visual design, architecture, implementation, production release, and continued improvement.
- Deep hands-on proficiency in TypeScript, React, and Node.js, with the ability to explain, debug, and maintain the full application and the code generated by AI.
- Strong practical experience with MongoDB, Docker, Azure, Git, and CI/CD; REST APIs, service boundaries, authentication, authorization, and automated testing.
- A portfolio or case studies showing visual direction you personally created and implemented; strong product judgment, stakeholder communication, and the ability to simplify complex workflows.
Required AI development experience
You use AI coding tools and agents as a repeatable part of professional software delivery. Be ready to demonstrate the following:
- Substantial hands-on experience with one or more tools such as Claude Code, Cursor, OpenAI Codex, or GitHub Copilot, supported by production work you personally shipped.
- Effective agentic coding: define the task, establish acceptance criteria, guide work across relevant files and services, inspect changes, and correct the approach when needed.
- Practical context engineering: give agents the relevant requirements, repository context, architectural constraints, and examples. Maintain concise project instructions and reusable workflows where they improve results.
- Independent verification of AI output through code review, type checks, appropriate automated tests, and direct inspection of the application. Identify invented APIs, missed requirements, security issues, and regressions.
- Good judgment about when to delegate work to AI, when to investigate directly, and when to reject or rework a proposed solution. Balance tool capability, cost, speed, and reliability.
- Careful handling of company and customer information, credentials, tool permissions, and production access when using AI tools.
Nice to Have
- Experience building AI-powered application features using LLM APIs, structured outputs, tool calling, retrieval, or model evaluations.
- Experience improving AI-development workflows through reusable agent instructions, automated checks, or Model Context Protocol (MCP) integrations.
- Experience with MSP operations, enterprise administration, or internal business applications.
Benefits
- Competitive salary range
- Paid time off and company holidays
- Health, dental, and vision insurance options
- 401(k) retirement plan with company match
- On-site gym for employee wellness
- Modern, collaborative office environment
- Ongoing training and professional development
- Opportunity to work with cutting-edge technology
- Team events and employee appreciation initiatives
- Strong company culture focused on innovation and teamwork