About the job
The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.
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
Qualifications
Minimum
- Bachelor’s degree or above in Computer Science 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
Preferred
- Familiarity with evaluation frameworks, prompting / finetuning / RL, or large-scale experimentation