Research Engineer Graduate (Agent Systems & AI Coding Environment – Seed Infra) – 2026 Start (PhD)

About the Team:

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Build agent harness and execution environments for AI coding and knowledge tasks (code execution, tool integration, sandboxing, system interaction)
• Develop scalable orchestration frameworks for multi-step agent workflows (planning, tool use, memory, coordination)
• Design evaluation and benchmarking systems to measure agent performance across complex, long-horizon tasks
• Improve agent performance via prompting, data curation, and post-training in collaboration with model and RL teams
• Partner with research and product teams to productionize agent systems

Requirements:

Minimum Qualifications:

• Individuals who are completing or have recently completed a Ph.D. in Computer Science, Machine Learning, Systems, or a related field

• Strong programming skills (Python or similar) and solid system-building experience

• Experience with LLM-based systems, pipelines, or tool-integrated workflows

• Understanding of system design and building scalable infrastructure

• Experience building agent harness / runtime systems or AI coding environments

• Experience with Docker, Kubernetes or similar orchestration systems, distributed job execution, and containerized/sandboxed code execution environments

• Preferred Qualifications

• Familiarity with evaluation frameworks, prompting / finetuning / RL, or large-scale experimentation