Member of Technical Staff - Intern

Summary

NeoSigma is a product-driven research lab focused on building the intelligence layer that connects customers, products, and AI systems. They are seeking a Member of Technical Staff - Intern to design and implement LLM-powered systems, work on self-improving AI problems, and build data processing pipelines while collaborating closely with customers and the core team.

Responsibilities

• Design and implement LLM-powered systems and scalable agentic workflows in production
• Work on open-ended problems in self-improving AI: agentic systems, auto-evals, verifier research, and benchmarks
• Build robust data processing pipelines for evaluation datasets and feedback loops
• Run experiments, iterate fast, and push ideas from hypothesis to working system
• Work closely with customers and the core team to deeply understand workflows and deliver meaningful impact
• Publish findings through technical blogs, research papers, or open artifacts that strengthen our technical foundation

Skills

• Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Engineering, or a related field
• Hands-on experience building LLM-powered or agentic systems, in production or research settings
• Strong research taste, especially in evaluation design, post-training methods, and model improvement workflows
• Desire to own problems end-to-end, from first idea to shipped artifact
• Obsession with detail across research rigor, code quality, and clean modular design
• Comfortable working directly with a small, fast-moving team

Qualifications

Must Haves

• Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Engineering, or a related field
• Hands-on experience building LLM-powered or agentic systems, in production or research settings
• Strong research taste, especially in evaluation design, post-training methods, and model improvement workflows
• Desire to own problems end-to-end, from first idea to shipped artifact
• Obsession with detail across research rigor, code quality, and clean modular design
• Comfortable working directly with a small, fast-moving team