Founding AI Engineer - Full Stack Background

About the Role

We are looking for a hands-on, high-ownership Founding Software Engineer to help build our core product from the ground up. In this role, you will bridge the gap between cutting-edge AI research and production-grade software engineering. You will own product engineering end-to-end—translating architectural designs into scalable backend systems, robust APIs, and functional frontend components.As an early hire, you will work directly with the founders and our AI Research team to shape both our technology stack and our engineering culture as we scale.

What You'll Do

• Own Product Engineering 01: Architect, build, and deploy features end-to-end—taking them from initial concept and user problem definition to iteration in live production environments.
• Bridge AI Research & Production: Partner closely with AI Research Scientists to transform experimental research outputs, models, and prototypes into reliable, enterprise-ready features.
• Build AI & Agentic Core Infrastructure: Design and implement the critical APIs and backend services that connect our LLM problem-formulation layer, algorithmic solvers, and knowledge libraries.
• Architect High-Performance Workloads: Design systems optimized for asynchronous, long-running, and compute-intensive workloads typical of agentic AI execution.
• Manage Infrastructure & Security: Own cloud deployments, CI/CD pipelines, system monitoring, and site reliability while ensuring strict security standards for sensitive enterprise data.
• Drive Strategic Technical Decisions: Make pragmatic build-vs-buy decisions, select framework components, and maintain a high-velocity development pace.
• Set the Engineering Standard: Establish foundational development best practices across code reviews, automated testing, and documentation that future hires will build upon.
• Deliver Full-Stack Value: Build practical, intuitive frontend components when necessary to help users interact with and clearly interpret the platform's complex AI outputs.

What We Look For

• Proven Startup Execution: 3–7 years of software engineering experience (or equivalent hands-on experience), with a demonstrated track record of taking a product, feature, or system from 01 in an early-stage environment.
• Autonomy & Comfort with Ambiguity: Ability to operate independently with high technical judgment, making architecture and implementation decisions amid changing requirements.
• The Collaboration & Translation Layer: Ability to work closely with researchers or technical teams, effectively translating complex technical work into usable product features.
• Strong Communication: Clear articulation of technical trade-offs and architectural reasoning to both technical and non-technical stakeholders.
• A Builder Mindset: Pragmatic, hands-on, and fully willing to jump across different parts of the stack to solve the problem at hand.
• Educational Background: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field.

Technical Expertise

Backend & Infrastructure Integration

• Core Languages: Strong experience with Python, Node.js, Go, or similar robust backend technologies.
• API Development: Deep experience building, deploying, and designing RESTful APIs and complex backend services.
• Systems Architecture: Hands-on experience with databases, asynchronous task queues, and handling long-running or compute-intensive workloads.
• Cloud & DevOps Ownership: Experience owning cloud platforms (AWS, GCP, or Azure) alongside modern DevOps workflows including CI/CD, containerization, logging, and monitoring.
• System Interoperability: Experience integrating APIs, third-party services, or deeply intricate technical subsystems.

AI / LLM Integration

• AI Applications: Direct experience with LLMs, AI applications, or agentic systems is a strong plus.
• LLM API Orchestration: Experience integrating mainstream LLM APIs (e.g., OpenAI, Anthropic, Gemini) or open-source models is highly advantageous.
• Workflow Automation: Familiarity with AI agents, LLM orchestration, or custom AI agentic workflows is advantageous.
• Research to Production: Proven ability turning AI/ML research code or raw prototypes into production-grade systems is a major plus.

Frontend Engineering

• Framework Familiarity: Practical experience with a modern frontend framework such as React, Vue, or Svelte.
• Data Visualization & UX: Ability to build or modify frontend components that connect to backend APIs to present dense, structured insights and model reasoning clearly to the user.