Ready to shape the future of LLM development? Join us in building the next generation of AI observability tools!
Responsibilities:
Build & ship features at high pace in a fast-moving AI development environment
Develop and maintain comprehensive observability tools for LLM applications and RAG systems
Create intuitive user interfaces for LLM tracing, evaluation dashboards, and production monitoring
Build robust APIs and backend services to support real-time LLM application monitoring
Collaborate with AI researchers and ML engineers to implement cutting-edge evaluation methodologies
Contribute to both frontend (React/TypeScript) and backend (Java) components
Design and implement automated evaluation systems and "LLM as a Judge" capabilities
Work on integrations with popular LLM frameworks and libraries
Participate in open-source community engagement and technical documentation
Experience: 3+ years of full-stack development experience, preferably in AI/ML domains
AI Development Tools: Hands-on experience with AI-assisted coding environments, IDEs, and agent-based workflows (e.g., Cursor, Windsurf, GitHub Copilot, Codex, Claude Code, and similar platforms)
Model Knowledge: Deep understanding of different AI model capabilities, limitations, and appropriate use cases (GPT-5, Claude, Gemini, etc.)
Frontend Development: Expertise in React, TypeScript and modern web development practices
Backend Development: Strong proficiency in Java with frameworks like Dropwizard
Database & Infrastructure: Knowledge of scalable database design and containerization
Observability Tools: Experience with tracing, monitoring, and evaluation systems
Collaboration: Strong communication skills for working in a distributed, global team environment
Problem Solving: Ability to work in ambiguous environments and solve complex technical challenges
Nice to have:
Open Source: Experience contributing to or maintaining open-source projects
AI/ML Experience: Understanding of Large Language Models, RAG systems, and AI application architectures
Preferred Qualifications:
Experience with model evaluation, prompt engineering, and LLM optimization
Knowledge of distributed systems and high-throughput data processing
Familiarity with ML experiment tracking and model monitoring platforms
Experience with DevOps practices and CI/CD pipelines
Understanding of AI safety, model security, and responsible AI practices
