Software Inference Deployment Engineer

Lumai is redefining how the world computes. We are an ambitious, venture-backed UK startup pioneering a breakthrough AI accelerator for data centers which uses 3D optical compute. Our radical technology uses light to perform computation at orders of magnitude faster speeds and at far greater scales than ever before, all whilst consuming far less energy than traditional approaches.

Lumai is unlocking performance and efficiency gains that could transform the economics of AI and compute infrastructure and reshape how intelligence scales globally.

If you are passionate about bringing groundbreaking technology to market, and want to be part of a team pushing the boundaries of what is physically possible, Lumai is where you can make it happen.

Founded in 2022, Lumai is a University of Oxford spinout using optical processing to accelerate large language models (LLMs) and other transformer-based AI systems. The team combines expertise in optical computing, machine learning, and physics.

Lumai has already secured over $15 million in investment from leading deep-tech investors like Constructor Capital, IP Group, PhotonVentures and government grants, and is scaling rapidly to deploy the fastest optical compute currently available globally.

We are bringing the world's first optical AI compute platform to market. As we move from development into field deployment, we are looking for a Software Inference Deployment Engineer to own the software-side integration and customer support of Lumai Iris servers in third-party data centre environments.

You will begin by working alongside our software and engineering teams - helping integrate the Iris software stack, supporting model onboarding through the toolchain, and getting hands-on with the disaggregated prefill/decode runtime. This is intentional: the best way to develop deep expertise in a novel platform is to build with it. As deployments go live, you will take ownership in the field - supporting customer integration into their inference stacks, troubleshooting software issues, and acting as a primary technical contact for customer ML and infrastructure engineering teams.