Founding Engineer Intern

Internship Role, with potential to convert to a full-time position

About Factryze

Factryze is building an AI-native infrastructure software layer for GPU clusters. Our platform combines deep observability with agentic AI to help teams identify failures, diagnose performance bottlenecks, and improve efficiency across large-scale AI training and inference workloads.

You will work at the intersection of data centres, GPU infrastructure, AI and ML workloads, observability, backend systems, frontend dashboards, and agentic AI workflows.

This is a hands-on engineering role for someone who enjoys building systems from the ground up, solving unfamiliar problems, and shipping products that address complex infrastructure challenges.

This role is expected to evolve into a full-time position, subject to mutual alignment and performance.

Website: https://www.factryze.ai

What We Are Looking For

We are looking for someone with strong engineering fundamentals and a practical, problem-solving mindset.

You should have:

• Strong fundamentals in JavaScript, TypeScript, and Python
• Working knowledge of React and modern frontend development patterns, including component architecture, state management, and reactivity
• Experience building and consuming REST APIs
• A good understanding of how frontend, backend, and infrastructure layers work together
• Comfort working in Linux environments, reading logs, and debugging technical issues
• Experience building and shipping real-world projects, including personal, academic, open-source, internship, or professional projects
• A strong problem-solving mindset and a willingness to work with unfamiliar systems
• The ability to write clean, maintainable, and reviewable code

You do not need to have prior expertise in GPU clusters, but you should be interested in learning how modern AI infrastructure works.

What You Will Work On

You will help build core features across the Factryze platform, including frontend dashboards, backend services, diagnostic workflows, and internal tools.

Your responsibilities may include:

• Building new features and systems for Factryze’s AI-native GPU infrastructure platform
• Developing frontend dashboards for GPU, system, workload, and cluster-level metrics
• Creating reusable UI components for charts, tables, alerts, logs, and real-time status views
• Building backend APIs for data ingestion, querying, diagnostics, and workflow orchestration
• Working with time-series data, high-volume metrics, logs, and infrastructure events
• Integrating visualisations for performance, utilisation, failures, and workload health
• Collaborating on agentic AI workflows for automated infrastructure diagnosis
• Debugging production issues across frontend, backend, and infrastructure layers
• Improving the reliability, maintainability, and developer experience of the platform
• Writing clean, practical code that is shipped to real users

Nice to Have

The following experience is helpful but not mandatory:

• Experience with FastAPI, Flask, or similar Python backend frameworks
• Familiarity with Docker and containerised development
• Exposure to monitoring and observability tools such as Prometheus, Grafana, metrics, logs, or traces
• A basic understanding of GPUs, AI and ML workloads, distributed systems, or cluster infrastructure
• Experience building dashboards, analytics tools, internal tools, or infrastructure products
• Familiarity with real-time updates, WebSockets, streaming data, or polling-based systems
• Interest in AI agents, automated diagnostics, and infrastructure automation

Why Join Factryze

GPU clusters are expensive, complex, and difficult to operate efficiently. Factryze is building software that helps infrastructure teams understand what is happening across their systems, identify issues faster, and improve performance through intelligent automation.

At Factryze, you will work on meaningful engineering problems across the full stack, including frontend dashboards, backend APIs, metrics pipelines, AI-powered diagnostics, and infrastructure debugging.