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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Researcher in AI Computing Systems - **Company:** Eu Recruit - **Location:** Zurich, Netherlands - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Nvidia CUDA, Linux Kernel, Linux System Administration, Large Language Models, Machine Learning Operations - **Published:** June 13, 2026 - **Apply:** https://eu-recruit.com/jobs/senior-researcher-ai-computing-systems-2/ ## About the Role * PhD in Computer Science, Electrical Engineering, or a related field. * Strong software engineering skills in Python, with deep experience in PyTorch. * Solid understanding of transformer inference, including: + Prefill vs decode stages + KV-cache structure and memory layout + Masking and batching strategies + Latency vs throughput trade-offs * Experience with benchmarking and profiling large-scale LLM workloads. * Ability to diagnose and resolve performance bottlenecks. * Strong communication skills and ability to collaborate across research and engineering teams. Preferred Qualifications * Experience working with modern LLM inference frameworks (e.g., vLLM-like systems or similar). * Familiarity with attention kernel development and optimization: + CUDA, Triton, or custom kernel implementations * Experience building or optimizing RAG pipelines, including: + Retrieval and indexing + Chunking and reranking + Interaction between retrieval and inference latency * Contributions to open-source projects or publications in AI systems or ML infrastructure. * Systems-level expertise, including: + Linux environments + Memory hierarchy and storage systems + Performance engineering close to hardware Personal Attributes * Strong systems-thinking mindset with attention to performance and scalability. * Ability to bridge research concepts and production engineering. * Detail-oriented with a focus on measurable performance improvements. * Collaborative approach in multidisciplinary environments. * Curiosity and drive to explore emerging AI infrastructure techniques. ## Description A research-driven technology organization is seeking a Senior Researcher in AI Computing Systems to advance the efficiency of large language model (LLM) inference and retrieval-augmented generation (RAG) pipelines. This role operates at the intersection of systems research and low-level performance engineering, focusing on optimizing attention mechanisms, KV-cache strategies, and end-to-end inference stacks. The position involves translating cutting-edge research into high-performance, production-ready implementations., LLM Inference Optimization * Design and implement techniques to reduce inference latency and improve throughput, including: + KV-cache precomputation + Cache reuse and blending strategies + Efficient batching and scheduling * Optimize time-to-first-token (TTFT) and overall system efficiency. KV-Cache Systems & Memory Optimization * Develop and integrate KV-cache reuse and blending pipelines into inference systems. * Design caching policies including: + Paging and eviction strategies + Memory layout optimization + Trade-offs between accuracy and performance * Ensure correctness and stability under high-throughput workloads. Attention Mechanism Optimization * Implement and optimize sparse and selective attention techniques. * Develop efficient masking strategies and block-level computation methods. * Work closely with attention kernels to maximize hardware utilization. Low-Level Performance Engineering * Profile and optimize model execution using modern attention backends and kernel frameworks. * Work with: + PyTorch internals + High-performance attention kernels (e.g., FlashAttention-style implementations) * Identify and resolve performance bottlenecks across compute and memory subsystems. Research Translation & Innovation * Stay current with advances in LLM inference, caching systems, and RAG architectures. * Translate research ideas into robust, scalable implementations. * Contribute to internal innovation and potentially to external publications or open-source projects. ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models) - [An Applied Introduction to eBPF with Go](https://www.wearedevelopers.com/videos/1075-an-applied-introduction-to-ebpf-with-go) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)