R&D Engineer in AI Accelerator Optimization

About KRAI KRAI is a cutting-edge AI infrastructure optimization company, a proven and valuable strategic partner for top accelerator designers, server manufacturers, and cloud providers. We are a Founding Member of MLCommons, actively contributing to community research and open-source efforts for AI Systems. We are looking for R\&D engineers to advance the state-of-the-art in AI accelerator programming. The core challenge? Mapping rapidly evolving AI workloads onto rapidly evolving AI hardware (GPUs and next-generation accelerators), while navigating an infinite space of performance, quality, and cost trade-offs. Our approach combines rigorous performance engineering with systematic agentic techniques. We aim for results that genuinely surprise even seasoned professionals. What You'll Do * Developing and optimizing low-level compute kernels for the latest AI workloads. * Working across a range of accelerator architectures, including hardware that is years from public release. * Exploring performance, efficiency, and quality trade-offs. * Driving full-stack inference optimization: from AI models all the way down to hardware. * Applying both traditional performance engineering tools (compilers, profilers, roofline models, simulators) and frontier AI techniques to solve complex optimization problems. * Collaborating with top accelerator designers, server manufacturers and cloud providers to deliver best-in-class performance results. What We're Looking For * Advanced degree (MSc or PhD) in Computer Engineering, Computer Science, or Natural Sciences. * 3+ years of hands-on experience optimizing compute-intensive workloads on accelerator hardware (GPU, FPGA, or similar). * Strong command of performance engineering tools: compilers, debuggers, profilers, simulators, and roofline analysis. * Experience with full-stack AI inference optimization: from models to runtimes to kernels. * Strong communication and collaboration skills. Why KRAI * Always at the bleeding edge: working with the latest AI models and pre-release accelerator hardware. * Real-world impact: directly influencing hardware roadmaps and procurement decisions at major technology companies. * Active contributions to open-source and research: getting high visibility and recognition in the AI Systems community. * Small well-knit team with deep technical expertise and friendly culture.