Lumai
AI Accelerator Product Systems Engineer

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The Opportunity
Lumai has moved from development into field deployment. Iris servers are in customers' hands for evaluation, which means they now have to be integrated, brought up and supported in data centres Lumai does not own, by teams who have never seen a system like this. This hire is what makes that repeatable.
The Role
You will own the hands-on technical delivery of Iris servers end to end. You begin alongside the core engineering team, owning the integration, validation and performance characterisation of units before they ship, and then you take ownership in the field: system bring-up, performance characterisation and post-deployment support in third-party data centre environments, where you are the primary technical contact for the operators and the customer.
What is genuinely hard about this is that you are putting a genuinely novel compute platform into production for the first time. There is no playbook to follow and many of the failure modes are new to everyone, and when one appears the customer is standing next to you: you are the first line of response, on site or remotely, working out whether the problem is the unit, the rack, the network or the workload.
By month six, you will have taken units through integration and validation into live data centre deployments, turned what you learned into a bring-up and troubleshooting playbook the team can reuse, and trained the operator teams who run them day to day.
What You'll Do
- Integrate and build Lumai Iris server units with the engineering team, owning bring-up, validation and performance characterisation ahead of deployment.
- Lead system bring-up, validation and performance characterisation of units in third-party data centre environments.
- Own on-site and remote troubleshooting of hardware issues as the first line of response after deployment.
- Train and enable data centre operators and customer engineering teams on the Iris platform.
- Act as the primary post-deployment technical contact for customers and data centre operators.
- Feed field findings, deployment issues and customer feedback back into product and engineering.
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.
What We're Looking For
Must-have
- Hands-on experience in a systems engineering, field engineering or hardware deployment role within AI infrastructure, HPC or comparable hardware.
- Practical experience with system integration, bring-up and hardware-level troubleshooting.
- Familiarity with data centre environments: rack power, thermal and networking.
- Comfortable in a customer-facing role, able to communicate clearly with operators and engineering teams alike.
- Comfortable working in a fast-moving, early-stage environment where the product and the deployment playbook are both still being written.
Strong preference for
- Experience with AI inference hardware or accelerated compute systems.
- Enough performance analysis experience to characterise results and explain them to customer teams.
- Genuine curiosity about novel compute architectures and post-silicon approaches.
- Programming literacy (for example Python) sufficient to engage credibly on benchmark methodology and basic automation.
- Experience in a deep-tech, semiconductor or hardware startup environment.
About Lumai
Lumai is the optical compute company building the next generation of AI infrastructure. Spun out of optics research at the University of Oxford in 2021, we compute with light instead of electrons. Our 3D optical technology carries out the matrix multiplications at the heart of AI inside beams of light travelling through free space, which lets it go beyond the limits of both silicon GPUs and integrated photonics.


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In April 2026 we launched Iris Nova, the world's first optical computing system to run billion-parameter large language models in real time, using up to 90% less energy than conventional GPU-based systems. Iris Nova, the first server in the family, is now available for evaluation by hyperscalers, neoclouds, enterprises and research institutions. Aura and Tetra will follow.
Our work won the Falling Walls Award for Science Breakthrough of the Year 2025 and 'Best Overall Technology' at the OCP Future Technologies Symposium. We are headquartered in Oxford.
Why Lumai
- You'll work on a new kind of computer. Optical computing for AI has been promised for decades. We have a working system running real models, and the hard part left is taking it to volume.
- Your work ships. We are moving from first product to volume production, so what you build this year goes into the servers our customers run.
- You'll work across disciplines. Optical engineers, machine learning researchers, and hardware and software engineers solve problems together. You will learn things that don't appear on your job description.
- The work matters beyond Lumai. AI's appetite for energy is one of the defining constraints of the next decade. Our mission is sustainable intelligence at global scale: AI that is faster, cheaper to run and far less power-hungry.
- You'll join early. You'll have a real say in how we build the product, the team and the way we work.
Equal Opportunity
Lumai is an equal opportunity employer. We make hiring decisions based on skills, experience and potential, and we welcome applications from people of all backgrounds. If you need an adjustment at any stage of the hiring process, let us know and we will do our best to support you.
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