About the job
The Red Hat Performance and Scale Engineering team is seeking a Senior Performance Engineer to join the Performance and Scale for AI Platforms (PSAP) team. In this role, you will help drive the performance and scalability of distributed inference for Large Language Models (LLMs) as part of the llm-d open source project.
Responsibilities
Define and track key performance indicators (KPIs) and service level objectives (SLOs) for large-scale, distributed LLM inference services in Kubernetes/OpenShift
Participate in the performance roadmap for distributed inference, including multi-node and multi-GPU scaling studies, interconnect performance analysis, and competitive benchmarking
Formulate performance test plans and execute performance benchmarks to characterize performance, drive improvements, and detect performance issues through data analysis and visualization
Develop and maintain tools, scripts, and automated solutions that streamline performance benchmarking tasks.
Collaborate with cross-functional engineering teams to identify and address performance issues.
Partner with DevOps to bake performance gates into GitHub Actions/OpenShift Pipelines.
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling.
Triage field and customer escalations related to performance; distill findings into upstream issues and product backlog items.
Publish results, recommendations, and best practices through internal reports, presentations, external blogs, and official documentation.
Represent the team at internal and external conferences, presenting key findings and strategies.
Qualifications
Minimum
5+ years of overall software engineering experience, including at least 3 years focused on performance engineering or systems-level development.
Strong understanding of operating systems and distributed systems
Foundational knowledge of AI and LLM inference workflows
Proficiency in Python for data and machine learning workflows, along with strong Linux and Bash skills
Excellent communication skills, with the ability to translate performance data into clear business and customer value
Passion for and commitment to open source principles
Preferred
Master’s or PhD in Computer Science, AI, or a related field
Experience contributing to open source projects or leading community initiatives
Hands on experience with Kubernetes or OpenShift
Familiarity with performance and observability tools such as perf, eBPF tools, Nsight Systems, and PyTorch Profiler
Experience with modern LLM inference stacks such as vLLM, TensorRT LLM, Hugging Face TGI, and Triton Inference Server