Senior Software Engineer (AI Systems Performance)

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.

SKILLS

Must have

• 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.

Nice to have

ACADEMIC CREDENTIALS: Master’s or PhD degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.