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