- Conduct research and build prototypes for next-generation AI systems spanning large-scale training, post-training, reinforcement learning, inference, retrieval, and agentic workloads.
- Study bottlenecks and design trade-offs across the stack, including communication, scheduling, storage, observability, runtime efficiency, memory behavior, and serving infrastructure.
- Develop benchmarking and evaluation methodologies that capture not only raw performance, but also system efficiency, scalability, robustness, and cost under realistic AI workloads.
- Explore new system designs in areas such as distributed training and RL systems, inference optimization, retrieval and memory architectures, agent infrastructure, and heterogeneous hardware-software co-design.
- Work closely with researchers and engineers to turn ideas into prototype systems, empirical insights, and technical proposals that can influence future infrastructure directions.
- Communicate results through clear technical writing, experimental analysis, and collaborative discussion.
Example Research Directions You may work on one or more of the following areas, depending on background and project fit:
- Distributed Training, Post-Training, and RL Systems
Study the systems challenges behind large-scale model development pipelines, including communication bottlenecks, cluster efficiency, scheduling, checkpointing, rollout infrastructure, and resource orchestration for RL and post-training workloads.
- Inference and Serving Systems
Design systems for low-latency, high-throughput, and cost-efficient serving of large models, including KV cache management, batching, runtime optimization, memory efficiency, elastic scaling, and accelerator-aware serving architectures.
- Retrieval, Memory, and Long-Horizon Agent Infrastructure
Explore infrastructure support for retrieval-augmented systems, AI memory architectures, and long-horizon agents, including vector indexing, memory organization, state management, and coordination across multi-stage agent workflows.
- Storage, Networking, and Observability for AI Workloads
Develop system designs for high-performance storage, cluster networking, observability, fault localization, and root cause analysis in large-scale AI environments where training and serving workloads operate under tight performance constraints.
Investigate how learning-based methods and AI agents can improve infrastructure diagnosis, tuning, orchestration, scheduling, and system optimization for large-scale AI workloads.