Full Stack AI Engineer Intern
HyperOrbit
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Job summary
HyperOrbit is seeking a Full Stack AI Engineer to own end-to-end product development, from elegant UI to scalable backend services. You will integrate AI agents, design real-time experiences, dashboards, and collaboration workflows, and ensure seamless interaction between interfaces, data sources, and third-party tools.
The role emphasizes high ownership and rapid delivery in a fast-paced startup environment.
Job description
We're looking for a Full Stack AI Engineer to build the end-to-end product experience that powers HyperOrbit's autonomous AI workforce. You'll own both frontend and backend development, turning complex AI agent capabilities into intuitive, powerful workflows that customers use every day.
We're looking for a Full Stack AI Engineer to build the end-to-end product experience that powers HyperOrbit's autonomous AI workforce. You'll own both frontend and backend development, turning complex AI agent capabilities into intuitive, powerful workflows that customers use every day. You'll work across the entire stack—from designing elegant user interfaces and real-time agent experiences to building APIs, AI integrations, data systems, and scalable backend services. This is a high-ownership role for someone who enjoys moving quickly, solving ambiguous problems, and taking products from idea to production. This role is critical to our mission: enabling companies to deploy, manage, and collaborate with autonomous AI agents that work 24/7.
- Build intuitive interfaces for users to deploy, configure, and manage autonomous AI agents
- Design real-time experiences that show what agents are doing, thinking, and executing
- Create agent workspaces for reviewing outputs, approving actions, and collaborating with AI
- Build interfaces for configuring agent instructions, goals, knowledge, tools, and workflows
- Develop dashboards that visualize agent performance, activity, outcomes, and ROI
- Create human-in-the-loop workflows for agent review, approval, and intervention
- Own the development of product features from frontend experience through backend APIs and infrastructure
- Build responsive, high-performance web applications for enterprise users
- Design scalable APIs and backend services powering the HyperOrbit platform
- Create reusable frontend and backend components that accelerate product development
- Build multi-tenant architectures supporting multiple customers, teams, agents, and workspaces
- Ensure seamless interactions between the user interface, AI agents, data sources, and external tools
- Integrate LLMs and AI models into production product experiences
- Build systems for agent orchestration, tool calling, memory, and workflow execution
- Create interfaces and backend services for managing agent context and knowledge
- Implement Retrieval-Augmented Generation (RAG) and semantic search capabilities
- Build evaluation and observability systems to monitor agent quality and performance
- Design feedback loops that help improve agent accuracy and outcomes over time
- Build real-time interfaces for agent activity, notifications, and task updates
- Implement streaming responses and live status updates for AI interactions
- Create collaborative workspaces where teams and AI agents can work together
- Build notification and alerting systems across web, Slack, email, and other channels
- Develop workflow automation experiences connecting agents with business tools
- Build frontend and backend experiences for connecting customer data sources
- Develop integrations with tools such as Salesforce, HubSpot, Slack, Gong, Zendesk, Jira, and Linear
- Create APIs and webhook systems for bi-directional data exchange
- Build data ingestion and processing workflows for agent knowledge and context
- Implement secure authentication and authorization for third-party integrations
- Own features end-to-end, from product requirements and UX implementation to backend architecture and deployment
- Build polished, responsive frontend experiences that make complex AI capabilities simple to use
- Design and develop scalable backend services and APIs
- Write clean, maintainable, and well-tested code across the stack
- Rapidly prototype new AI product experiences and turn successful experiments into production features
- Continuously improve application performance, reliability, and user experience
- Build modern web applications using React, Next.js, or similar frameworks
- Create reusable component systems and scalable frontend architecture
- Implement complex workflows, dashboards, data visualizations, and interactive AI interfaces
- Build responsive experiences optimized for desktop and mobile
- Implement real-time UI updates using WebSockets, Server-Sent Events, or streaming APIs
- Collaborate closely with product and design to translate ideas into exceptional user experiences
- Optimize frontend performance, accessibility, and usability
- Design and build APIs and backend services powering the HyperOrbit platform
- Develop scalable systems for users, workspaces, agents, tasks, workflows, and integrations
- Build authentication, authorization, and role-based access control systems
- Design database schemas and optimize data access patterns
- Implement background jobs, queues, and asynchronous workflows
- Build reliable systems for processing AI requests and agent actions
- Ensure platform security, scalability, and reliability
- Integrate leading LLM providers and open-source models into the product
- Build agent workflows involving planning, reasoning, tool use, memory, and execution
- Implement RAG pipelines using vector databases and enterprise knowledge sources
- Develop prompt management and structured output systems
- Build agent evaluation frameworks to measure quality, reliability, and performance
- Implement guardrails, validation, and fallback mechanisms for production AI systems
- Optimize AI applications for latency, quality, and cost
- Work directly with founders and product teams to shape product direction
- Turn ambiguous ideas into functional prototypes quickly
- Make thoughtful product and technical tradeoffs
- Identify friction in the user experience and proactively improve it
- Participate in customer feedback sessions and translate insights into product improvements
- Help define how users interact with and manage an AI workforce
- Collaborate with AI engineers on agent intelligence and model performance
- Partner with product and design teams to build intuitive user experiences
- Work with Solutions and Customer Success teams on enterprise implementations
- Collaborate with engineering teams on platform architecture and scalability
- Support customers and internal teams with debugging and technical problem-solving
- 1+ years of experience building and shipping full-stack web applications
- Strong proficiency in JavaScript or TypeScript
- Strong experience with React and modern frontend frameworks such as Next.js
- Strong backend development experience with Python, Node.js, or similar technologies
- Experience designing and building production APIs and backend services
- Strong understanding of relational databases and SQL
- Experience building scalable SaaS applications
- Strong product mindset with the ability to think beyond individual engineering tasks
- Excellent problem-solving and debugging skills
- Comfortable working in a fast-paced startup environment with high ownership
- Deep experience with React, Next.js, or similar modern frontend frameworks
- Strong understanding of component architecture and state management
- Experience building complex dashboards and data-heavy applications
- Familiarity with modern UI systems and component libraries
- Experience with real-time interfaces and streaming data
- Strong understanding of frontend performance optimization
- Knowledge of responsive design and accessibility best practices
- Experience building APIs using FastAPI, Django, Node.js, Express, or similar frameworks
- Strong experience with PostgreSQL and database design
- Familiarity with Redis, caching, and asynchronous processing
- Experience with background jobs and message queues
- Understanding of authentication and authorization systems
- Experience building and consuming third-party APIs and webhooks
- Familiarity with cloud infrastructure and deployment workflows
- Experience building production applications using LLMs
- Familiarity with OpenAI, Anthropic, Gemini, or open-source models
- Experience with AI agent frameworks and orchestration patterns
- Understanding of prompt engineering and structured outputs
- Experience with RAG architectures and vector databases
- Familiarity with embeddings, semantic search, and knowledge retrieval
- Experience building AI applications with tool calling and external integrations
- Understanding of AI evaluation, observability, and guardrails
- Frontend: React, Next.js, TypeScript, Tailwind CSS
- Backend: Python (FastAPI) and/or Node.js
- Databases: PostgreSQL, Redis, MongoDB
- AI/LLM: OpenAI, Anthropic, LangChain, LangGraph, or similar
- Vector Database: Pinecone, Weaviate, pgvector, or similar
- Cloud: AWS, GCP, or Azure
- Real-Time: WebSockets, Server-Sent Events, streaming APIs
- Message Queues: RabbitMQ, Apache Kafka, AWS SQS, or similar
- CI/CD: GitHub Actions or similar
- Containerization: Docker and Kubernetes
- Monitoring: Sentry, Datadog, OpenTelemetry, or similar
- Version Control: Git and GitHub
- Built AI-native SaaS products from scratch
- Experience building autonomous or semi-autonomous AI agents
- Experience with multi‑agent systems
- Built products involving human‑in‑the‑loop AI workflows
- Experience with AI observability and evaluation platforms
- Experience building enterprise SaaS products with multi‑tenancy
- Strong eye for product design and user experience
- Experience working directly with founders or in an early‑stage startup
- Experience taking products from prototype to production
- Contributed to open-source AI or developer tools
- Experience with knowledge graphs or graph databases
- Familiarity with enterprise security and compliance requirements (SOC 2, GDPR)
Frontend: React + Next.js + TypeScript + Tailwind CSS
Backend: Python + FastAPI
Databases: PostgreSQL + Redis
AI/Agents: LLM APIs + Agent Orchestration + Tool Calling + RAG
Vector Search: Pinecone or pgvector
Cloud Infrastructure: AWS (ECS, Lambda, RDS, S3, SQS)
Real-Time: WebSockets + Server-Sent Events
Message Queue: RabbitMQ / Apache Kafka / AWS SQS
Monitoring: Datadog + Sentry + OpenTelemetry
Authentication: Auth0 or equivalent with OAuth 2.0 + SSO
CI/CD: GitHub Actions
Containerization: Docker + Kubernetes
- Competitive market‑rate salary
- Meaningful equity in a high‑growth startup
- Annual performance bonuses
- Remote‑first, flexible work culture
- Flexible working hours across timezones
- Paid time off and team retreats
- Small, high‑impact team where your work matters
- Direct collaboration with founders
- High ownership across product and engineering
- Shape both the product and technical architecture
- Move quickly from idea to production
- Build the future of how companies deploy and work alongside AI agents
At HyperOrbit, we're building an AI‑native platform designed around a simple idea: AI agents shouldn't just answer questions—they should do meaningful work.
As a Full Stack AI Engineer, you'll be at the center of that mission. You'll build the interfaces, infrastructure, and intelligence that allow businesses to deploy autonomous AI agents across their workflows.
This isn't a role where you'll spend years maintaining legacy systems. You'll work on hard, unsolved problems at the intersection of AI, software, and product—and see your work directly shape the future of the company.
If you love building products end‑to‑end, experimenting with AI, and turning ambitious ideas into working software, we'd love to talk.
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