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.
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