Senior AI systems performance
Luxoft · São Paulo, Brazil
**Project Description:** * A leading semiconductor and high\-performance computing company is looking for a software engineer who is passionate about Distributed Inferencing on GPUs and improving the performance of key applications and benchmarks. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology **Responsibilities:** * We are seeking a software engineer with strong technical expertise in C\+\+/ Python development, solving performance and investigating scalability on multi\-GPU, multi\-node clusters. He is also passionate about quality assurance, benchmarking, and automation in the AI/ML space. The ideal candidate thrives in both collaborative and independent environments, demonstrates excellent problem\-solving skills, and takes ownership in defining goals and delivering impactful solutions. * KEY RESPONSIBILITIES: * Distributed AI Enablement and Benchmarking: Enable and benchmark AI models on large\-scale distributed systems to evaluate performance, accuracy, and scalability. * Scalable Systems Optimization: Optimize AI workloads across scale\-up (multi\-GPU), scale\-out (multi\-node), and scale\-across distributed system configurations. * Cross\-Team Collaboration: Collaborate closely with internal GPU library teams to analyze and optimize distributed workloads for high throughput and low latency. * Parallelization Strategies: Develop and apply optimal parallelization strategies for AI workloads to achieve best\-in\-class performance across diverse system configurations. * Model Infrastructure and Management: Contribute to distributed model management systems, model zoos, monitoring frameworks, benchmarking pipelines, and technical documentation. * Performance Monitoring and Visualization: Build and maintain real\-time dashboards reporting performance, accuracy, and reliability metrics for internal stakeholders and external users. **Mandatory Skills:** * General\-Purpose computing on GPUs (GPGPU) **Mandatory Skills Description:** * PREFERRED EXPERIENCE: * AI Framework Engineering: Hands\-on experience with AI inference or serving frameworks such as vLLM, SGLang, and Llama.cpp. * KV Cache and Expert Parallelization: Understanding KV cache transfer mechanisms and technologies (e.g., Mooncake, NIXL/RIXL) and expert parallelization approaches (e.g., DeepEP, MORI, PPLX\-Garden). * Programming and Software Design: Strong C/C\+\+ and Python skills, with experience in software design, debugging, performance analysis, and test development. * Large\-Scale Distributed Systems: Experience running AI workloads on large\-scale, heterogeneous compute clusters. * Cluster and Orchestration Systems: Familiarity with cluster management and orchestration platforms such as SLURM and Kubernetes (K8s). * Development Tools and Workflows: Experience with GitHub, Jenkins, or similar CI/CD tools and modern development workflows.