软件工程实习生
Software Engineering Intern
作为软件工程实习生，你将主要负责我们AI产品的应用层开发。
你将构建前端和后端功能、API、集成、工作流以及其他与AI模型和推理服务相连的产品组件。
你将与经验丰富的工程师一起，在使用TypeScript、Node.js、React、Next.js、数据库、API和云服务等技术的真实生产系统上工作。
你还将了解诸如LLMs、RAG流程、AI代理和私有模型推理等系统如何被集成到生产应用中。
这是一个注重实践的工程岗位。你将需要编写代码、调试问题、理解所工作的系统，并逐步承担产品中更大部分的责任。
你将参与的工作内容包括：
根据项目不同，你可能会参与：
- 构建前端和后端产品功能。
- 设计和使用REST API及内部服务。
- 操作数据库、认证、文件存储和第三方集成。
- 将应用程序与AI模型、推理API、RAG系统和AI代理连接。
- 调试前端、后端和集成中的应用程序问题。
- 编写测试并提升现有功能的可靠性。
- 使用云平台和现代部署工具进行部署和维护应用程序。
- 与经验丰富的工程师审查你的工作，并根据技术反馈进行改进。
你不会被限制在孤立的实习练习中。目标是让你为正在实际构建和使用的软件做出贡献。
我们更看重你的工程基础和学习能力，而不是简历中列出的框架数量。
你需要具备：
- 扎实的编程基础和解决问题的能力。
- 至少掌握一种编程语言。
- 对网页应用、API、数据库和Git有基本理解。
- 通过课程项目、个人项目、实习、黑客松或其他类似工作，至少完成一个有意义的软件项目。
- 能够理解现有代码并对其进行修改。
- 愿意深入调查问题，而不是停留在看似可行的第一个解决方案上。
- 在遇到困难、对方法有异议或需要更多上下文时，能够清晰地进行沟通。
熟悉JavaScript/TypeScript、React、Node.js或类似技术会有所帮助，但我们不要求实习生已经掌握我们的技术栈。
查看英文原文
As a Software Engineering Intern, you will work primarily on the application layer of our AI products.
You will build frontend and backend features, APIs, integrations, workflows, and other product components that connect with AI models and inference services.
You will work with experienced engineers on real production systems using technologies such as TypeScript, Node.js, React, Next.js, databases, APIs, and cloud services.
You will also gain exposure to how systems such as LLMs, RAG pipelines, AI agents, and private model inference are integrated into production applications.
This is a hands-on engineering role. You will be expected to write code, debug problems, understand the systems you are working on, and gradually take ownership of larger pieces of the product.
What You'll Work On
Depending on the project, you may work on:
- Building frontend and backend product features.
- Designing and consuming REST APIs and internal services.
- Working with databases, authentication, file storage, and third-party integrations.
- Connecting applications with AI models, inference APIs, RAG systems, and AI agents.
- Debugging application issues across frontend, backend, and integrations.
- Writing tests and improving the reliability of existing features.
- Deploying and maintaining applications using cloud platforms and modern deployment tools.
- Reviewing your work with experienced engineers and improving it based on technical feedback.
You will not be restricted to isolated internship exercises. The objective is for you to contribute to software that is actually being built and used.
We care more about your engineering fundamentals and ability to learn than the number of frameworks listed on your resume.
You should have:
- Good programming fundamentals and problem-solving ability.
- Working knowledge of at least one programming language.
- Basic understanding of web applications, APIs, databases, and Git.
- Experience building at least one meaningful software project through coursework, a personal project, internship, hackathon, or similar work.
- Ability to understand existing code and make changes to it.
- Willingness to investigate problems instead of stopping at the first solution that appears to work.
- Ability to communicate clearly when you are stuck, disagree with an approach, or need more context.
Familiarity with JavaScript/TypeScript, React, Node.js, or similar technologies will help, but we do not expect interns to already know our complete technology stack.
Using AI as an Engineer
We use AI tools as part of our engineering workflow, and you are welcome to use them.
We do not evaluate engineers based on how much code they can type from memory.
What matters is whether you can understand the code you produce, question incorrect suggestions, test your implementation, debug failures, and explain the decisions you made.
AI can help you write code. You are still responsible for the result.
What This Role Is Not
This is not an AI research or model-training role.
Your primary responsibility will be software and product engineering at the application layer.
You will work close to our AI engineering team and gain visibility into model inference, RAG, agents, and Private AI infrastructure, but your core focus will be building the applications that make those capabilities useful.
This is also not an internship where you spend your time only watching tutorials or completing disconnected training assignments.
You will learn by working on real engineering problems with support from the team.
Why Join Abstrabit
You will get the opportunity to build software in an environment where traditional application engineering and modern AI systems meet.
You will:
- Work on real AI-powered products rather than demo projects.
- Learn how production applications interact with private and open-weight AI models.
- Work across frontend, backend, APIs, databases, cloud infrastructure, and AI integrations.
- Receive regular technical feedback and code reviews from experienced engineers.
- Gradually take ownership of larger features as your engineering ability improves.
- Get exposure to both the application engineering and AI engineering sides of modern AI systems.
For engineers early in their careers, we believe understanding both sides will become increasingly valuable.
Our Interview Process
Step 1: Application Screening - We review basic eligibility, projects, and evidence that you have spent time building and experimenting with technology.
Step 2: Practical Engineering Assessment & Discussion - You will work through a practical engineering problem and discuss your approach with one of our engineers.
We may ask you to explain your solution, debug an issue, make a small change, or reason through an alternative approach. We are interested in how you use available tools, validate your work, and respond when something does not work as expected.
Step 3 - Final Conversation
A discussion about the role, your learning goals, expectations, and whether Abstrabit is the right environment for you.
Abstrabit is a Private AI engineering company building AI-powered products and solutions for businesses. We work across the AI stack — from deploying and evaluating private and open-weight models to building production applications, agents, workflows, and integrations on top of them.
Our engineering work is organized across two closely connected layers: the AI layer, focused on models, inference, evaluation, and AI infrastructure; and the application layer, focused on building reliable, scalable software products that use those AI capabilities.
The Context
AI models are only one part of a production AI system.
They still need applications around them — APIs, workflows, user interfaces, authentication, databases, integrations, monitoring, and reliable production infrastructure.
Our Software Engineering team owns this application layer.
As we build more AI-powered products, we are looking for engineers who want to become strong software developers while working close to modern AI systems.
Originally posted on Himalayas