Research Engineer - Agent Training Infrastructure; Seed Infra

Position: Research Engineer - Agent Training Infrastructure (Seed Infra)

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. Responsibilities - Design, implement, and maintain agent execution environments and runtime frameworks for multi-agent training at scale - Build and optimize infrastructure for RLHF pipelines, reward modeling, and distributed RL training - Manage and orchestrate many-agent parallel execution, including environment simulations and environment managers - Collaborate closely with research teams to support the LLM training pipeline: training → SFT → RLHF → evaluation → serving - Ensure high-performance, scalable, and fault-tolerant distributed systems for agent frameworks - Develop tools and libraries to monitor, debug, and benchmark agent training and inference - Translate research prototypes into production-ready infrastructure that can support large-scale AI experiments

Minimum Qualifications - M.S. or Ph.D. in Computer Science, Machine Learning, or a related field - Strong experience with Python and distributed systems frameworks (e.g., Ray) - Hands-on experience building agent infrastructure: execution environment, runtime, or environment manager - Experience managing parallel multi-agent execution, including simulations and environment orchestration - Familiarity with the LLM pipeline (training → SFT → RLHF → evaluation → serving) - Proven ability to design and maintain high-performance, scalable, and robust distributed AI systems

Preferred Qualifications - Experience building or contributing to RLHF pipelines, reward modeling infrastructure, or RL training infrastructure - Strong understanding of multi-agent reinforcement learning and agent orchestration at scale.

• Familiarity with GPU clusters, distributed training strategies, and performance optimization - Publications or open-source contributions in agent systems, distributed RL, or AI infrastructure.

• Experience mentoring engineers and collaborating in cross-functional research and engineering teams