AI Systems Engineer (OCI/AI Infrastructure)
- Compensation
- Not disclosed
- Location
- Nashville, TN, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Solid knowledge of AI / GPU or/and AI/CPU platform architecture and their capabilities.
- Experience with the architecture, design, and implementation of modern server platforms consisting of multiple architectures and vendors, including x86 and ARM server architectures.
- Strong communications skills and ability to clearly communicate complex technical issue across engineering disciplines as well as clearly and succinctly articulate issues for executives.
- Experience and understanding of the latest high-speed busses and interconnect used in modern Compute and AI platforms. Familiarity with their startup connectivity and operational robustness as well as
- Debugging & Reliability: Troubleshoot complex hardware–software interaction issues, including vLLM compilation failures on ROCm, CUDA memory leaks, distributed runtime failures, and kernel-level incon
- Profiling & Performance Analysis: Conduct detailed profiling of compilation graphs, training workloads, and runtime execution to optimize performance and eliminate bottlenecks.
Nice to have
- Minimum of 8+ years of experience in developing software infrastructure for large scale AI systems.
- Bachelor's degree or higher in Computer Science or a related technical field (or equivalent experience).
- Strong debugging skills and experience in analyzing and triaging AI applications from the application level to the hardware level.
- Hands-on experience maintaining or building ML training stacks involving CUDA, ROCm, NCCL, XLA, or similar technologies.
- Experience in benchmarking AI workloads across different architectures.
- Background in working with the large scale clusters
- Good understanding on DL frameworks internal PyTorch, TensorFlow, JAX, and Ray
Full description
Oracle Hardware Platform Development Engineering is seeking a highly driven AI Systems Engineer to evaluate and characterize next-generation GPU and AI accelerator platforms for Oracle Cloud Infrastructure (OCI). This is a hands-on engineering role focused on bringing up new hardware platforms, enabling AI training and inference software stacks, running representative workloads, and analyzing system performance under real operating conditions.
The engineer will identify whether workloads are HBM/memory-bandwidth, compute, scale-up, or scale-out bound, while characterizing power, thermals, memory behavior, utilization, scaling, and performance efficiency. Working directly in the lab, you will debug hardware/software integration issues, design and execute experiments, and develop data-driven insights that explain system behavior beyond benchmark results.
A key part of the role is comparative architecture analysis across GPUs and emerging AI accelerators. You will evaluate architectural tradeoffs and translate performance findings into clear, actionable recommendations on which platforms are best suited for specific AI training and inference workloads. You will work closely with internal hardware and software teams as well as technology partners to help shape Oracle’s next generation of high-performance AI infrastructure.
Position Overview:
This position is ideal for someone who loves deep systems engineering, debugging complex hardware–software interactions, and optimizing performance at every layer of the ML stack. You will play a pivotal role in enabling the training and deployment of next-generation LLMs and generative AI models.
Required Qualifications
- Solid knowledge of AI / GPU or/and AI/CPU platform architecture and their capabilities.
- Experience with the architecture, design, and implementation of modern server platforms consisting of multiple architectures and vendors, including x86 and ARM server architectures.
- Strong communications skills and ability to clearly communicate complex technical issue across engineering disciplines as well as clearly and succinctly articulate issues for executives.
- Experience and understanding of the latest high-speed busses and interconnect used in modern Compute and AI platforms. Familiarity with their startup connectivity and operational robustness as well as performance metrics.
- Debugging & Reliability: Troubleshoot complex hardware–software interaction issues, including vLLM compilation failures on ROCm, CUDA memory leaks, distributed runtime failures, and kernel-level inconsistencies.
- Profiling & Performance Analysis: Conduct detailed profiling of compilation graphs, training workloads, and runtime execution to optimize performance and eliminate bottlenecks.