Software Engineer (R&D Engineer is the Internal Title) - 9 Month FTC
Department Overview
The Engineering department at InfoSum is responsible for designing and delivering innovative data collaboration and AI technologies that power our platform and enable privacy-safe, AI enabled, data partnerships at global scale. Alongside core product development, the department invests in research and experimentation to explore emerging technologies, validate new architectural approaches, and prototype potential future capabilities of the platform. The R&D function plays a key role in bridging research ideas with practical engineering solutions, helping transform promising concepts into prototypes and technical foundations that can evolve into production systems.
Job Overview
As an R&D Engineer, you will work closely with the Chief Architect and engineering leadership to explore new technical ideas, build prototypes, and evaluate emerging technologies that could shape the future direction of the platform. A key part of the role will also involve continuing the development of existing internal research initiatives and evolving them into more mature technical prototypes and early product foundation. This role combines hands‑on engineering with exploratory research. You will be responsible for rapidly translating architectural ideas and research concepts into proof‑of‑concept implementations, experimental prototypes, and early‑stage systems that demonstrate feasibility and inform product decisions. In many cases you will take ownership of advancing research work beyond its initial concept stage, iterating on prototypes and expanding them into more complete technical capabilities. While this role is exploratory in nature, it also requires strong AI and engineering discipline. Successful prototypes often evolve into foundations for production systems, and therefore the role requires the ability to build clean, well‑structured and maintainable code while iterating quickly.
Core Responsibilities
- Prototype Implementation: Take architectural ideas and research concepts and turn them into working software prototypes. Build functional implementations that demonstrate how new platform capabilities could work in practice.
- Advancing Research Projects: Contribute to the continued development of existing internal research initiatives by expanding prototypes, improving implementations, and evolving early concepts into more complete technical solutions.
- Experimental Software Development: Design and implement experimental services, tools, agents and infrastructure components using solid engineering practices. Write clear and maintainable code that can serve as the foundation for future product development.
- Technical Exploration & Validation: Explore new frameworks, technologies, and architectural patterns through hands‑on development. Evaluate feasibility by building working implementations rather than purely theoretical analysis.
- Documentation & Knowledge Sharing: Document implementation approaches, experimental findings, and prototype architectures. Share insights and technical learnings with the broader engineering organization.
- Collaboration: Work closely with architects, engineers, product teams, and infrastructure teams to ensure prototypes and experimental systems can inform real product development.
- Continuous Learning: Stay informed about advances in distributed systems, AI systems, and modern software engineering practices.
Required Skills
- Experience with web development frameworks (e.g., React, Angular, Vue.js) and/or backend frameworks.
- Strong understanding of software development methodologies, design patterns and best practices.
- Familiarity with database technologies (e.g., SQL, NoSQL) and data modeling concepts.
- Knowledge of version control systems (e.g., Git) and CI/CD pipelines.
- Excellent problem‑solving skills, attention to detail, and ability to work independently and collaboratively in a fast‑paced environment.
- Effective communication skills, both written and verbal, with the ability to articulate technical concepts to non‑technical stakeholders.
- Previous experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).
- Experience with containerization technologies such as Docker and Kubernetes.
- Experience with large‑scale data systems or distributed processing frameworks.
- Experience building internal developer tools or platform infrastructure.
- Experience working with AI agents, LLM orchestration frameworks, or emerging AI infrastructure patterns.
- Curiosity about emerging technologies and a willingness to experiment with new approaches.
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