Research Engineer Graduate (Agent Evaluation & Training Infrastructure - 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

• Design and build scalable agent evaluation frameworks for multi-step, tool-using, and environment-interacting agents
• Develop reproducible rollout systems, including trajectory logging, execution formats, and experiment replay
• Build distributed execution pipelines for large-scale agent evaluation and post-training workloads
• Support RLHF and other post-training workflows through infrastructure for rollout, reward modeling, and policy optimization
• Develop sandboxed and cloud-based environments for safe, reliable agent experimentation
• Improve scalability, reliability, and performance of agent training and evaluation systems

Minimum Qualifications:

• Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Strong programming skills in Python and experience building large-scale systems or open-source frameworks
• Familiarity with reinforcement learning workflows (e.g., rollout, trajectory collection, policy optimization)
• Experience with distributed systems or parallel execution frameworks (e.g., Ray, PyTorch Distributed, Kubernetes)
• Strong understanding of scalability and system reliability

Preferred Qualifications

• Experience in agent systems, evaluation frameworks, or RLHF pipelines
• Open-source contributions or publications in ML systems, agent infrastructure, or distributed RL