Intern, Systems Software Engineering

Samsung Semiconductor • San Jose, CA

6d • Onsite

About The Position

The Samsung Global Ecosystem Team (GOST) seeks an intern to develop systems software for memory and storage pools supporting reinforcement-learning post-training of large language models. The project examines how rollout generation, reward evaluation, policy updates, and checkpoint recovery use shared resources. The intern will build a focused prototype using fabric-attached memory and an SSD pool, then measure effects on training progress, memory pressure, and recovery time.

GOST is a geographically distributed group of engineers contributing to open ecosystems such as the Linux kernel and QEMU. This internship supports software ecosystem R&D for Samsung’s latest memory and storage technologies through distributed open-source projects. The goal is to deliver a working memory-to-storage data path, a live RL post-training demonstration, and measured findings explaining when pooling improves training efficiency or recoverability. The position is based at Samsung’s San Jose headquarters, with daily onsite presence in accordance with the Flexible Work policy, and reports to a Principal Engineer.

Requirements

• Pursuing a master’s or PhD in Computer Science.
• Must have at least one academic quarter or semester remaining.
• Programming experience in C, C++, or Rust, with Python or shell scripting for experiments and automation.
• Understanding of operating systems, memory management, concurrency, and computer architecture.
• Experience developing and debugging on Linux, using Git, and communicating technical results; coursework and research projects count.
• Inclusive approach, adapting communication style to the situation and diverse global norms.
• Curiosity, resilience, and an evidence-seeking approach to challenges.
• Collaborative approach, including building relationships, offering support, and welcoming different approaches.
• Innovative and creative mindset, with willingness to explore new ideas and adapt quickly to change.

Nice To Haves

• Experience with CXL, NUMA, Device-DAX, mmap, shared-memory programming, or memory allocators.
• Experience with GPU programming, CUDA, DMA, or profiling memory and data-transfer performance.
• Experience with distributed systems, cache management, vLLM, SGLang, LLM inference, or open-source development.

Responsibilities

• Profile the RL post-training loop to identify memory-capacity limits, data-transfer overhead, storage stalls, and GPU idle time.
• Develop a runtime feature coordinating bounded memory staging, asynchronous storage writes, prefetching, or capacity allocation across workers.
• Integrate the feature with an existing RL framework using its training and checkpoint interfaces.
• Preserve snapshot consistency, policy-version provenance, buffer ownership, and durable checkpoint publication during asynchronous operations.
• Compare memory-only, storage-only, and combined pool configurations using training throughput, GPU-hours, local-memory usage, and restart performance.
• Deliver correctness tests, reproducible experiments, design documentation, and code suitable for review and potential open-source contribution.
• Identify and resolve a resource-management issue in the host OS for post-RL workloads.
• Complete other responsibilities as assigned.

Benefits

• Medical, dental, and vision benefits.
• Relocation stipend for those living more than 50 miles from the new office.
• Monthly housing stipend for the summer internship.
• Charitable giving match and opportunities to participate in community activities.
• On-demand emotional wellness apps and paid therapy sessions.
• Onsite café and gym, plus virtual fitness classes.
• Flexible environment to help employees find the right balance.

What This Job Offers

• Career Level: Intern