Senior Staff Engineer - AI Workloads & Storage

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 TogetherOur 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.At the Technology Enabling Development Lab (TED), our core development focus is the host interface firmware layer that sits in the intersection of system software and flash management firmware. This key host interface firmware technology drives Samsung’s breakthrough V-NAND technology and enables our customers to power performance-oriented, demanding, enterprise-class applications ranging from hyper-scale data centers, to big data processing, to software defined virtualized storage arrays and infrastructures.We are building the next generation of NAND/SSD storage systems designed for the demands of large-scale AI. As the compute cost of transformer inference falls, the bottleneck is shifting to how quickly and efficiently we can move model weights, KV cache, and activations through the storage hierarchy - and NAND flash and SSDs are increasingly the tier where that data lives. Our focus is on making SSDs first-class citizens in the AI data path, from the NAND media and flash-translation layer up through NVMe and networked storage.We are looking for a Sr Staff Engineer who lives at the intersection of AI inference systems and storage/systems software. This is a hands-on technical leadership role: you will characterize real AI workloads, translate what you learn into architecture, and drive that direction across inference, platform, and hardware teams. This is a rare seat for someone who is equally comfortable reading a transformer serving stack and a Linux block-layer trace.What You’ll DoOwn AI workload characterization. Profile production and emerging LLM inference, RAG, and training workloads to quantify their I/O, bandwidth, latency, and capacity demands, and turn those findings into concrete storage and memory-hierarchy design decisions.Identify optimal data placement. Analyze workload access patterns to determine how data should be placed and separated on flash, and map those insights onto SSD data-placement technologies such as NVMe Flexible Data Placement (FDP) and streams to reduce write amplification and improve endurance, latency, and QoS.Collaborate with key customers to identify differentiating SSD capabilities for AI workloads, and develop proof-of-concept implementations as part of those customer engagements - turning workload insights into demonstrable data-path, tiering, and data-placement wins.Lead deep-dive performance analysis spanning the inference runtime, the Linux storage and networking stack, and the underlying hardware, tuning for latency, throughput, cost, and GPU utilization.Build and evaluate transactional and system-level models of proposed architectures to de-risk decisions before hardware exists, and validate them against measured behavior.Engage with the standards and open ecosystem - SNIA (including Storage.AI), MLCommons/MLPerf, and the open inference stack - to align our work with where the industry is heading and to shape it where we can.Set technical direction others build on. Make build-vs-buy and architectural calls, establish benchmarking methodology and best practices, and mentor engineers across the org.Partner cross-functionally with product, hardware, and research teams, and with external vendors and partners, to bring architectures from concept to deployment.What You BringBachelor's degree 15+ years relevant industry experience or Master's degree 13+ years’ experience or PhD with 10+ years relevant industry experience.Extensive experience (typically 10–15+ years) in systems, storage, or ML-systems software, with a track record of architecting systems that materially improved performance, reliability, or cost.Demonstrated technical leadership and cross-team influence: setting direction, driving decisions across organizational boundaries, and mentoring senior engineers.Working knowledge of modern AI inference, especially transformer architectures - attention, KV cache, batching, and the memory/compute trade-offs of serving large models.Deep systems-level understanding of the Linux storage stack (block layer, I/O scheduling, NVMe) and of NAND/SSD internals (flash-translation layer, garbage collection, endurance/write-amplification, latency behavior), plus hands-on performance analysis skill (e.g., perf, ftrace, eBPF, blktrace, fio).Fluency in Python plus a systems language (C/C++, Rust, or Go).MS or PhD in Computer Science, Electrical/Computer Engineering, or a related field preferred - or equivalent practical experience.Preferred Qualification Hands-on experience with the modern inference stack: vLLM, SGLang, LMCache, NVIDIA Dynamo, TensorRT-LLM, or Triton.Familiarity with GPU-adjacent data movement and memory frameworks: NIXL, DOCA / DOCA MemOps, GPUDirect Storage, RDMA, NVMe-oF, and BlueField / DPU offload.Understanding of GPU and TPU architecture (memory hierarchy, interconnects, and how accelerator design shapes I/O and data-movement demands) is highly desired.Experience with user-mode storage access frameworks: SPDK, uNVMe, libvfn, or similar.SSD firmware experience - flash-translation layer, wear-leveling and garbage-collection algorithms, and data-placement features such as FDP / streams / ZNS - ideally paired with the ability to co-design firmware and host-side placement policy from workload characterization.AI-workload characterization and benchmarking experience, and familiarity with SNIA Storage.AI and MLCommons / MLPerf.Transactional / discrete-event or system-level modeling experience in frameworks such as SystemC, SimPy, or similar.Experience with SSD architecture and interfaces - NVMe (including ZNS, Flexible Data Placement / FDP), open-channel SSDs, computational storage - and with PCIe Gen5, CXL, and large-scale GPU-cluster storage (VAST, WEKA, Lustre, Ceph).#LI-VL1What We OfferThe pay range below is for all roles at this level across all US locations and functions. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. We also offer incentive opportunities that reward employees based on individual and company performance. 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/401k, our inclusive rewards plan empowers our people to care for their whole selves. An investment in your future is an investment in ours.Give Back With a charitable giving match and frequent opportunities to get involved, we take an active role in supporting the community.Enjoy Time Away You’ll start with 4+ weeks of paid time off a year, plus holidays and sick leave, to rest and recharge.Care for Family Whatever family means to you, we want to support you along the way—including a stipend for fertility care or adoption, medical travel support, and virtual vet care for your fur babies.Prioritize Emotional Wellness With on-demand apps and free confidential 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.Base Pay Range$189,000—$301,000 USDEqual 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 FairnessAt 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 PolicyWe 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 NoticeBy 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