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KRAI

R&D in AI Accelerator Optimization

Cambridge
Posted 1 day ago
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About Us

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 the non-profit MLCommons consortium, actively contributing to community research and open-source efforts for AI Systems.

We are looking for exceptional R&D engineers

The core challenge? Mapping rapidly evolving AI workloads onto rapidly evolving AI accelerator hardware (next generation accelerators, as well as traditional GPUs), 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 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.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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It searches the market for you

Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.

Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Strong

Only hits

No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.

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 (GPUs, TPUs, NPUs, etc).
  • Experience with full-stack AI inference optimization: from models to runtimes to kernels.
  • Strong command of performance engineering tools: compilers, debuggers, profilers, simulators, and roofline analysis.
  • Workflow automation and reproducibility as first-class concerns.
  • Strong communication and collaboration skills.

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What We're NOT Looking For

  • We do NOT design AI hardware: we optimize software that runs on our customers' hardware.
  • We do NOT design AI pipelines: we get down to the nitty-gritty of AI inference.

Why KRAI

  • Always at the bleeding edge: working with the SOTA 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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Skills

AI Accelerator Optimization
Compute Kernel Development
Full-stack AI Inference Optimization
Performance Engineering
Compiler Optimization
Roofline Analysis
GPU Optimization
TPU Optimization
NPU Optimization
Workflow Automation
Profiling
Debugging
Simulators
AI Workload Mapping

Location

Cambridge, England, United Kingdom

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