AI Application Engineer Intern

Samsung; Folsom (CA); On-site; USD 39,000 - 94,000; Full time

Benefits offered by this job

Relocation Support

Housing Stipend

Volunteer/community giving

Job summary

Samsung Semiconductor is seeking a hands-on intern to help develop and optimize next-generation SoC/SSD architectures. You will work on LLM inference services, RTL design integration, and validation on a GPU-accelerated stack.

The role is onsite in San Jose, CA, reporting to a Senior Director and collaborating with storage, FPGA, and firmware engineers. The position emphasizes practical experimentation, data-driven bottleneck analysis, and cross-team communication.

Qualifications

• Pursuing Bachelor's, Master's or PhD in CS/CE/EE with at least one academic quarter/semester remaining.
• Hands-on experience running LLM inference with Python and PyTorch on NVIDIA GPUs under Linux.
• Understanding LLM serving internals: KV cache, batching, latency metrics, and throughput.
• Experience benchmarking and profiling system performance with scripts (Python, pandas) to explain bottlenecks.
• Familiarity with KV cache offloading, RDMA/NVMe-oF storage, and containers (Docker/Kubernetes).
• You are inclusive, curious, collaborative and innovative.

Responsibilities

• Deploy and operate an LLM inference service (vLLM with Llama-3.3-70B) on 4 × NVIDIA H200 GPUs and benchmark latency/throughput.
• Build an automated benchmark harness for long-context, multi-turn and agent-style traces.
• Configure KV cache offloading to NVMe-oF storage and validate end-to-end data paths with FPGA/SoC teams.
• Run daily experiment sweeps across configurations and keep results reproducible in Git.
• Analyze GPU/network/SSD bottlenecks with Python and present findings in team meetings.
• Summarize storage requirements (bandwidth, IOPS, latency) for future SSD controllers.
• Collaborate across FPGA, SoC and firmware teams to debug data paths and validate KV cache correctness.
• Complete other responsibilities as assigned.

Skills

LLM inference with Python

PyTorch

Linux

Benchmarking

Profiling

KV cache offloading

RDMA/NVMeoF

Docker/Kubernetes

Collaboration

Education

Bachelor/Master/PhD in CS/CE/EE

Tools

vLLM

SGLang

Hugging Face TGI

Job description

Please Note:

To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.

Advancing the World’s Technology Together

Our technology solutions power the tools you use every day--including smartphones, electric vehicles, hyperscale data centers, IoT devices, and so much more. Here, you’ll have an opportunity to be part of a global leader whose innovative designs are pushing the boundaries of what’s possible and powering the future.

We believe innovation and growth are driven by an inclusive culture and a diverse workforce. We’re dedicated to empowering people to be their true selves. Together, we’re building a better tomorrow for our employees, customers, partners, and communities.

What You’ll Do

The SoC Architect team proposes multiple SoC level architecture candidates for emerging memory/storage solution and demonstrates that the proposed architecture is viable through hands-on RTL implementation.

This role offers the opportunity to shape next-generation SoC and subsystem architectures while staying deeply hands-on in RTL design, FPGA integration, and validation. It is ideal for engineers who want to turn advanced memory and storage concepts into real hardware and drive them all the way to silicon.

Location: Daily onsite presence at our San Jose, CA headquarters in alignment with our Flexible Work policy

Reports to:Senior Director

• Deploy and operate an LLM inference service (vLLM serving Llama-3.3-70B) on a 4 × NVIDIA H200 GPU server, working closely with your mentor and the storage architecture team.
• Build an automated benchmark harness that replays long-context, multi-turn and agent-style request traces and records latency and throughput (TTFT, TPOT, goodput).
• Configure KV cache offloading (LMCache, NVIDIA Dynamo/NIXL) to network-attached storage over NVMe-oF and RDMA, first on a BlueField-3 storage server and then on our network-attached SSD prototype.
• Run daily experiment sweeps across request rates, context lengths and cache sizes, keeping every configuration and result reproducible in git.
• Analyze results with Python to find bottlenecks across the GPU, network and SSD, and share findings in weekly team meetings.
• Collaborate with FPGA, SoC and firmware engineers to debug the end-to-end data path and validate KV cache correctness.
• Summarize the measured storage requirements (bandwidth, IOPS, latency) that shape the architecture of future SSD controllers, and present them to the team at the end of the internship.
• Complete other responsibilities as assigned.

What You Bring

• Pursuing Bachelors, Masters, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field preferred.
• Must have at least 1 academic quarter/semester remaining.
• Hands-on experience running LLM inference with Python and PyTorch on NVIDIA GPUs under Linux, using a serving framework such as vLLM, SGLang or Hugging Face TGI.
• Understanding of LLM serving internals: KV cache, prefix caching, batching and parallelism (tensor/pipeline), and latency metrics such as TTFT, TPOT and throughput.
• Experience benchmarking and profiling system performance, and analyzing results with scripts (e.g., Python, pandas, plotting) to explain bottlenecks.
• Familiarity with KV cache offloading (LMCache, NVIDIA Dynamo/NIXL), RDMA/RoCE or NVMe/NVMe-oF storage, and containers (Docker/Kubernetes) preferred.
• You’re inclusive, adapting your style to the situation and diverse global norms of our people.
• An avid learner, you approach challenges with curiosity and resilience, seeking data to help build understanding.
• You’re collaborative, building relationships, humbly offering support and openly welcoming approaches.
• Innovative and creative, you proactively explore new ideas and adapt quickly to change.

#LI-AD1

What We Offer

The pay range below is for all roles at this level across all US locations and functions. Paywithin this range varies by work locationand may also depend on job-related knowledge, skills,and experience.

This is in addition to our diverse package of benefits centered around the wellbeing of our employees and their loved ones. In addition to the usual Medical/Dental/Vision, our inclusive rewards plan empowers our people to care for their whole selves. An investment in your future is an investment in ours.

• Relocation Support Live 50+ miles away from your new office? No worries! We'll provide a stipend to help support your move.
• Housing Stipend Signing a lease on a new place for your summer internship? We'll help you pay your way with a monthly stipend.
• Give Back With a charitable giving match and frequent opportunities to get involved, we take an active role in supporting the community.
• Prioritize Emotional Wellness With on-demand apps and paid therapy sessions, you’ll have support no matter where you are.
• Stay Fit Eating well and being active are important parts of a healthy life. Our onsite Café and gym, plus virtual classes, make it easier.
• Embrace Flexibility Benefits are best when you have the space to use them. That’s why we facilitate a flexible environment so you can find the right balance for you.

Hourly Base Pay Rate $28—$68 USD

Equal Opportunity Employment Policy

Samsung Semiconductor takes pride in being an equal opportunity workplace dedicated to fostering an environment where all individuals feel valued and empowered to excel, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status.

When selecting team members, we prioritize talent and qualities such as humility, kindness, and dedication. We extend comprehensive accommodations throughout our recruiting processes for candidates with disabilities, long-term conditions, neurodivergent individuals, or those requiring pregnancy-related support. All candidates scheduled for an interview will receive guidance on requesting accommodations.

Our Commitment to Innovation and Fairness

At Samsung Semiconductor, we use Artificial Intelligence (AI) tools in the recruitment process to enhance efficiency. However, AI is used as a support tool, not a final decision-maker. All hiring decisions are made by our human recruiting team and hiring managers to ensure every candidate is evaluated fairly and holistically.

Recruiting Agency Policy

We do not accept unsolicited resumes. Only authorized recruitment agencies that have a current and valid agreement with Samsung Semiconductor, Inc. are permitted to submit resumes for any job openings.

Applicant AI Use Policy

At Samsung Semiconductor, we support innovation and technology. However, to ensure a fair and authentic assessment, we ask that candidates rely on their own knowledge and skills throughout the process. AI tools may be used for basic preparation, grammar, and research, but should not be used to generate or assist with submitted content or live interview responses. If we determine that AI is being used outside these guidelines, we reserve the right to pause or end the interview, and your candidacy may be disqualified.

Trade Secret Notice

By submitting an application, you agree not to disclose to Samsung—or encourage Samsung to use—any confidential or proprietary information (including trade secrets) belonging to a current or former employer or other entity.

Applicant Privacy Policy https://semiconductor.samsung.com/about-us/careers/us/privacy/