
1001 - 5000 employees
š¤ Artificial Intelligence
š§ Hardware
š° $300M GROWTH EQUITY VC on 2025-01
Artificial Intelligence ⢠Hardware
DDN is a global provider of high-performance data infrastructure and an advanced data intelligence platform built for demanding AI and GPU workloads. DDN delivers purpose-built hardware (e. g. , AI400X systems) and software solutions (EXAScaler, Infinia, Data Intelligence Platform) to power AI factories, sovereign AI, hyperscalers, and supercomputing environments, optimizing GPU utilization and end-to-end AI pipelines. The company serves enterprises and cloud providers across industries including automotive, financial services, life sciences, media & entertainment, higher education, and the public sector, emphasizing data sovereignty, multi-tenant cloud deployments, and performance at scale.
š„ 40 minutes ago
š² North Carolina, Texas ā Remote
ā° Full Time
š Senior
š§āš» Full-stack Engineer
š» Ghost score 15%
Where this job comes from
Posted on DDN's careers page, not through a job board
First seen by Remote Rocketship 40 minutes ago

1001 - 5000 employees
š¤ Artificial Intelligence
š§ Hardware
š° $300M GROWTH EQUITY VC on 2025-01
Artificial Intelligence ⢠Hardware
DDN is a global provider of high-performance data infrastructure and an advanced data intelligence platform built for demanding AI and GPU workloads. DDN delivers purpose-built hardware (e. g. , AI400X systems) and software solutions (EXAScaler, Infinia, Data Intelligence Platform) to power AI factories, sovereign AI, hyperscalers, and supercomputing environments, optimizing GPU utilization and end-to-end AI pipelines. The company serves enterprises and cloud providers across industries including automotive, financial services, life sciences, media & entertainment, higher education, and the public sector, emphasizing data sovereignty, multi-tenant cloud deployments, and performance at scale.
⢠Design, develop and debug LustreFS features, fixes and enhancements across llite, MDS/MDT, OSS/OST, LDLM and LNet ⢠Investigate customer and scale-related defects, drive root-cause analysis and implement high-quality fixes ⢠Contribute to performance tuning, failure analysis and reliability improvements for large-scale Lustre deployments ⢠Participate in code reviews, design reviews and subsystem discussions ⢠Work with QE and support to reproduce issues, improve diagnostic data quality and increase coverage for high-risk failure scenarios ⢠Document subsystem behavior, debugging approaches, known failure patterns and operational best practices ⢠Use AI-assisted tools to accelerate issue triage, log summarization, code understanding and reusable lessons learned ⢠Investigate customer-facing and scale-related issues across client, metadata, object storage, LNet and transport layers ⢠Design and implement reliability, observability and serviceability improvements ⢠Review and validate fixes through targeted tests, failure injection, log analysis and performance characterization ⢠Strengthen documentation, code review quality and subsystem knowledge sharing
⢠10+ years of experience in systems software, distributed systems, storage, Linux kernel or filesystem engineering ⢠Strong experience in LustreFS development, support or performance engineering, with depth in at least one major subsystem ⢠Strong C programming and Linux systems debugging skills ⢠Working knowledge of Linux kernel internals, filesystem semantics, networking and performance analysis ⢠Experience with LNet and/or high-performance transports such as RDMA, InfiniBand, RoCE or TCP-based storage networking ⢠Ability to debug and resolve issues spanning client, server, network and backend storage layers ⢠Strong collaboration skills and ability to work across functions in a fast-moving engineering environment ⢠Experience in HPC, AI infrastructure or large-scale parallel storage environments preferred ⢠Exposure to metadata-heavy and throughput-heavy workload characterization and tuning preferred ⢠Familiarity with ZFS, ldiskfs, NVMe-backed storage and observability/performance tooling preferred ⢠Experience creating test plans, reproducer frameworks, runbooks or diagnostic automation preferred ⢠Comfort using AI tools for debugging, code reviews, triage, documentation and early-stage design ideation preferred ⢠Experience mentoring junior engineers or leading focused technical efforts within a subsystem preferred
⢠No benefits, perks, or compensation extras are specified in the posting ⢠Application limit: up to 3 positions within a 60-day window ⢠Candidates are asked to wait 90 days before reapplying for the same position if it is not the right fit
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