Job Summary
The Red Hat Performance and Scale Engineering team is looking for a Senior Performance Engineer to join us in the PSAP (Performance and Scale for AI Platforms) team, driving the performance and scalability of distributed inference for Large Language Models (LLMs).
Serving modern LLMs for production inference requires distributing the model, the computation, and the requests across numerous specialized hardware accelerators across multiple nodes. This introduces complex performance challenges, from optimizing inter-GPU and inter node communication and kernel execution to minimizing latency under concurrent loads. You will be responsible for characterizing, modeling, and enhancing the performance of these distributed systems, ensuring that Red Hat’s AI platforms offer industry-leading throughput, latency, and cost-efficiency.
This role needs a seasoned engineer that thinks creatively, adapts to rapid change, and has the willingness to learn and apply new technologies. You will be joining a vibrant open source culture, and helping promote performance and innovation in this Red Hat engineering team. The border mission of the Performance and Scale team is to establish performance and scale leadership of the Red Hat product and cloud services portfolio. The scope includes component level, system and solution analysis and targeted enhancements. The team collaborates with engineering, product management, product marketing and customer support as well as Red Hat’s hardware and software ecosystem partners.
At Red Hat, our commitment to open source innovation extends beyond our products - it’s embedded in how we work and grow. Red Hatters embrace change – especially in our fast-moving technological landscape – and have a strong growth mindset. That’s why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers’ most pressing challenges.

What you will do:

• 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.

What you will bring:

• 3+ years in performance engineering or systems‑level software design
• Hands‑on expertise with Kubernetes/OpenShift
• Basic understanding of AI and LLMs fundamentals
• Fluency in Python (data & ML), strong Bash/Linux skills
• Exceptional communication skills - able to translate raw performance numbers into customer value and executive narratives
• Commitment to open‑source values

The following is considered a plus:

• Master’s or PhD in Computer Science, AI, or a related field
• History of upstream contributions and community leadership
• Familiarity with performance observability stacks such as perf/eBPF‑tools, Nsight Systems, PyTorch Profiler, among others
• Hands-on experience with modern LLM inference server stack (e.g., vLLM, TensorRT-LLM, TGI, Triton Inference Server)

LI-JS1

• 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