we are seeking a sharp, innovative, and hands-on Architect to help shape the future of LLM inference at scale. Join our dynamic E2E Architecture group, where we build cutting-edge systems powering the next generation of generative AI workloads. In this role, you will work across software and hardware domains to design and optimize inference infrastructure for large language models running on some of the most advanced GPU clusters in the world.
Youll help define how AI models are deployed and scaled in production, driving decisions on everything from memory orchestration and compute scheduling to inter-node communication and system-level optimizations. This is an opportunity to work with top engineers, researchers, and partners across our company and leave a mark on the way generative AI reaches real-world applications.
What Youll Be Doing:
Design and evolve scalable architectures for multi-node LLM inference across GPU clusters.
Develop infrastructure to optimize latency, throughput, and cost-efficiency of serving large models in production.
Collaborate with model, systems, compiler, and networking teams to ensure holistic, high-performance solutions.
Prototype novel approaches to KV cache handling, tensor/pipeline parallel execution, and dynamic batching.
Evaluate and integrate new software and hardware technologies relevant to model inference (e.g., memory hierarchy, network topology, modern inference architectures).
Work closely with internal teams and external partners to translate high-level architecture into reliable, high-performance systems.
Author design documents, internal specs, and technical blog posts and contribute to open-source efforts when appropriate.
Requirements: Bachelors, Masters, or PhD in Computer Science, Electrical Engineering, or equivalent experience.
5+ years of experience building large-scale distributed systems or performance-critical software.
Deep understanding of deep learning systems, GPU acceleration, and AI model execution flows.
Solid software engineering skills in C++ and/or Python, with strong familiarity with CUDA or similar platforms.
Strong system-level thinking across memory, networking, scheduling, and compute orchestration.
Excellent communication skills and ability to collaborate across diverse technical domains.
Ways to Stand Out from the Crowd:
Experience working on LLM inference pipelines, transformer model optimization, or model-parallel deployments.
Demonstrated success in profiling and optimizing performance bottlenecks across the LLM training or inference stack.
Familiarity with data center-scale orchestration, cluster schedulers, or AI service deployment pipelines.
Passion for solving tough technical problems and shipping high-impact solutions.
This position is open to all candidates.