Summer 2027 PhD AI Research Infrastructure, RL Post-Training Intern
Santa Clara, CA$91.5k - $137.3kUNAVAILABLEPosted 4w ago
About the role
Intern role focused on developing and optimizing RL-based post-training infrastructure for large language and multimodal models, with emphasis on scalable rollout, inference, training efficiency, and production-quality research infrastructure. Open to PhD students and designed as a full-time onsite Summer 2027 internship in Santa Clara, CA.
Responsibilities
- Develop and optimize infrastructure for RL-based post-training of large language and multimodal models.
- Build scalable systems for rollout generation, inference, reward computation, and policy updates.
- Improve distributed training efficiency, reliability, fault tolerance, and resource utilization.
- Design interfaces that enable researchers to implement and evaluate new RL algorithms quickly.
- Build tools for experiment configuration, checkpointing, logging, monitoring, and reproducibility.
- Profile end-to-end training pipelines and resolve performance, memory, and communication bottlenecks.
- Support on-policy and off-policy training workflows using verifiable, preference-based, or model-generated feedback.
- Collaborate with researchers to translate experimental requirements into production-quality infrastructure.
- Document system designs and contribute to technical reports and publications.
Qualifications
- Must be currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, or a related field.
- Strong programming skills in Python and experience with PyTorch.
- Knowledge of reinforcement learning, LLM post-training, RLHF/RLAIF, or preference optimization.
- Experience with distributed training, multi-GPU workloads, or large-scale inference.
- Familiarity with parallelism strategies such as data, tensor, pipeline, or sequence parallelism.
- Experience with training and inference frameworks, orchestration systems, or cluster environments.
- Understanding of GPU performance, memory management, networking, and distributed communication.
- Experience building reliable research infrastructure, profiling systems, or debugging distributed workloads.
- Familiarity with containerization, experiment tracking, and cloud or cluster computing is beneficial.
- Publications at leading venues such as ICML, NeurIPS, ICLR, MLSys, CVPR, ICCV, or ECCV are preferred.
Skills
Required:PythonPyTorchReinforcement LearningDistributed Training
Nice to have:Parallelism Strategies
Benefits
- AMD benefits at a glance (link provided in posting)
Last updated Oct 22, 2026. Interested? Apply on the company site.
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