IC Resources
AI Kernel Engineer

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A well-funded AI systems company is looking for engineers passionate about performance optimisation across next-generation AI hardware. The team partners with leading semiconductor companies, server manufacturers and cloud providers to maximise the performance and efficiency of modern AI systems.
This is an opportunity to work on cutting-edge accelerator architectures, solving complex performance problems long before hardware reaches production.
You’ll focus on optimising AI workloads across the hardware and software stack, developing low-level kernels and improving the performance of modern machine learning systems.
Working closely with a highly technical team, you’ll analyse AI models, profile workloads, identify bottlenecks and implement optimisations that deliver measurable improvements in throughput, latency and efficiency.
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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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour 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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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.
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.
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.
Responsibilities
- Optimise compute-intensive AI workloads across GPUs, AI accelerators and emerging hardware
- Develop and tune low-level kernels for machine learning applications
- Profile, benchmark and analyse system performance using industry-standard tools
- Identify performance bottlenecks across hardware and software layers
- Work with customers and engineering teams to deliver performance improvements
- Contribute to AI benchmarking and performance evaluation initiatives
- Explore new approaches to accelerator programming and AI-assisted optimisation


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Background
- Degree in Computer Science, Computer Engineering, Mathematics, Physics or a related technical discipline
- Experience optimising workloads on GPUs, AI accelerators, FPGAs or other high-performance hardware
- Strong understanding of performance analysis, profiling and benchmarking
- Experience with profiling, debugging and performance analysis tools
- Background in inference optimisation or performance-critical AI systems
- Strong C++ and/or Python programming skills
- Excellent communication skills with the ability to work in a collaborative, research-focused environment
Please contact Harry Hansford for more information.
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