About the Role
We are building cutting-edge AI systems that enable the next generation of intelligent applications. Our team focuses on developing AI agents, benchmarking datasets, and robust infrastructure to support real-world deployments at scale. As part of our engineering-driven culture, you'll be exposed to a fast-paced environment where experimentation, collaboration, and innovation are encouraged.
Responsibilities
- Contribute to the design and development of AI agents integrated into live systems.
- Help build and maintain benchmark datasets to evaluate AI performance.
- Implement, test, and optimize backend services in Python or Golang.
- Collaborate closely with senior engineers on system design, multi-agent orchestration, and deployment
- Participate in code reviews, debugging, and documentation to ensure best practices.
- Work in a team environment that encourages mentorship, knowledge sharing, and hands-on learning
Requirements
- Currently pursuing a degree in Computer Science, Software Engineering, or a related field
- Strong programming fundamentals — language-agnostic, but practical experience with at least one of Python, Go, TypeScript, or similar
- Solid understanding of software engineering principles (data structures, algorithms, version control)
- Familiarity or working knowledge of any AI agent framework (e.g., OpenClaw, hermes-agent, LangChain, CrewAI, AutoGen)
- Interest in AI, data-driven systems, and building tools for real-world applications
- Good communication skills and willingness to learn quickly
What We're Looking For (Culture & Mindset)
- AI-native working style — you naturally reach for AI tools in your daily workflow, not as an afterthought but as a core part of how you think, build, and solve problems
- Eager to learn and open-minded — you stay current with the latest technologies and aren't afraid to adopt new frameworks, tools, or paradigms before they're mainstream
- First-principles thinker who's comfortable redesigning workflows rather than just optimizing existing ones
Nice to Haves
- Experience with LLMs, prompt engineering, or building tools on top of AI models
- Exposure to multi-agent systems or agent orchestration patterns
- Familiarity with Docker, cloud platforms, or CI/CD pipelines
- Machine learning knowledge is a plus, but not required