Research Engineer - Reinforcement Learning (RL) Systems & 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 and build end-to-end reinforcement learning (RL) systems for large-scale models, covering rollout, training, evaluation, and deployment pipelines. - Develop scalable and fault-tolerant RL infrastructure that operates efficiently under dynamic workloads and heterogeneous compute environments. - Optimize distributed training performance across GPU clusters, improving throughput, resource utilization, and system stability. - Collaborate with cross-team researchers on targeted system-algorithm co-design to translate research ideas into robust, production-grade implementations. - Build tooling, monitoring, and debugging frameworks to ensure reliability and observability of large-scale RL training systems.Minimum Qualifications: - Strong background in distributed systems, large-scale ML systems, or deep learning infrastructure - Experience building or optimizing large-scale training systems (e.g., RL, LLM, multimodal models) - Solid engineering skills in Python/C++ and familiarity with modern ML stacks (PyTorch, distributed training frameworks, etc.) - Experience with GPU optimization, parallelism strategies, and system-level performance tuning - Understanding of reinforcement learning workflows (rollout, policy update, evaluation loops) Preferred Qualifications: - Experience with large-scale agent systems - Familiarity with system design under heterogeneous or dynamic workloads - Exposure to RL + LLM training or post-training pipelines