Backend Engineering Intern - Typescript/Nest.js, Postgres, Docker, Microservices, AI/ML

Backend Engineering Internship - Typescript/Nest.js, Postgres, Docker, Microservices, AI/ML

Remote | Unpaid Internship | 3-6 months | 15–20 hrs/week | Master's Degree Required

THIS IS NOT A SUMMER INTERNSHIP, for immediate hire only

We’re building a modular backend system designed for scalability, performance, and intelligent automation. Our team is focused on designing data-driven infrastructure that supports real-time user interactions, search, and ML-powered recommendations. We’re looking for backend-focused interns with degrees in Computer Science, Artificial Intelligence, or Machine Learning to join us in shaping the MVP and contributing to intelligent system development.

Why This Internship Is Unique

Unlike typical backend internships focused solely on bug fixes or minor tickets, this role provides exposure to:

• A production-grade microservices architecture built with NestJS (TypeScript)
• Integrations with modern platforms and developer tools (e.g., CMS, payments, identity providers)
• API Gateway patterns and communication between services via REST and message brokers
• PostgreSQL optimization, indexing, and integration with Algolia search engine
• Early-stage development of intelligent backend microservices using AI/ML

What You’ll Actually Work On

• Building and refining backend microservices in NestJS (TypeScript)
• Developing secure authentication flows and permission layers
• Implementing APIs and integrations for structured data management
• Managing PostgreSQL queries, indexes, and data pipelines for performance
• Writing modular, scalable backend code that supports future AI integration

AI/ML Development Responsibilities

You’ll also support the development of intelligent backend systems such as:

• Recommendation Models: Creating logic for suggesting entities (e.g., projects, tags, roles) based on profile or usage behavior
• Semantic Search: Enhancing query matching with vector similarity or keyword embeddings
• Trend Detection: Exploring models to detect spikes or shifts in usage behavior or category engagement
• Smart Scoring: Defining scoring logic for profiles, items, or events using ML models or rule-based systems
• LLM Integration: Collaborating with product to test prompt-based features using tools like HuggingFace

These ML-focused responsibilities are exploratory but ongoing and will run in parallel to core backend development.

What You Might Touch (If Time Allows)

As we scale, you may gain exposure to:

• Caching systems (e.g., Redis)
• Tracing tools (e.g., Jaeger, OpenTelemetry)
• GraphQL implementations with Apollo Server
• Event-driven architecture with RabbitMQ or Kafka
• Service-to-service security and observability layers

Minimum Qualifications

• Pursuing or recently completed a Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, or related field
• Experience in backend development with:

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• TypeScript/Node.js (preferably NestJS)
• Relational databases like PostgreSQL
• REST APIs and scalable integrations
• Familiar with GitHub workflows and QA testing standards
• Interest in combining backend development with AI libraries

Nice-to-Have

• Experience using Python, scikit-learn, or HuggingFace
• Exposure to vector databases, transformers, or recommendation frameworks
• Interest in scalable backend infrastructure and microservices system design

What You’ll Get

• Practical startup experience contributing to real product architecture
• Opportunity to build real-world AI/ML integrations from scratch
• Exposure to scalable backend infrastructure and early-stage platform development
• Mentorship from technical leads and async collaboration practices
• Reference letters and portfolio-worthy contribution

How to Apply

• Include your LinkedIn, GitHub, or portfolio
• Tell us why you're interested in backend engineering and how you'd like to apply AI/ML in a product setting
• Confirm that you are pursuing or have completed a Master's Degree in Computer Science, AI, or Machine Learning