Software Inference Deployment Engineer
Role details
Job location
Tech stack
Job description
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.
This is an opportunity to work at the cutting edge of efficient AI inference - deploying a genuinely novel compute platform into production for the first time, and playing a central role in how it reaches the world., * Work alongside Lumai's software and engineering teams to integrate, test, and harden the Iris software stack ahead of deployment
- Support model onboarding through the Iris toolchain - loading, conversion, and framework integration
- Develop hands-on familiarity with the disaggregated prefill/decode runtime, including how Iris servers operate alongside decode processors
- Support customer integration of Lumai Iris into their own frameworks
- Own software-side troubleshooting in the field, acting as the first line of response post-deployment
- Train and enable customer ML and infrastructure engineering teams on the Iris software platform
- Feed field findings, integration issues, and customer feedback back into product and engineering
Requirements
Do you have experience in Software deployment?, Do you have a Master's degree?, Must-Have
- Hands-on software engineering experience in AI infrastructure, inference serving, accelerator integration, or comparable deep-tech hardware-software environments
- Strong Python skills and familiarity with major ML frameworks (PyTorch in particular)
- Practical experience with model deployment workflows - loading, format conversion, quantisation, or framework integration
- Comfortable working with inference serving stacks (for example vLLM, TensorRT-LLM, or similar)
- Familiarity with Linux, containerisation (Docker), and cluster environments
- Comfortable in a customer-facing role, able to communicate clearly with ML and infrastructure engineering teams
- Comfortable working in a fast-moving, early-stage environment where the product and the deployment approach are both still being developed
Strong Preference For
- Experience integrating accelerator hardware (GPUs, FPGAs, ASICs, NPUs, or novel architectures) into customer inference workflows
- Familiarity with the NVIDIA inference stack - CUDA, TensorRT, Triton
- Exposure to disaggregated inference architectures, prefill/decode separation, or KV cache management
Benefits & conditions
Pulled from the full job description
- Annual leave
- Company pension
- Private medical insurance
- Cycle to work scheme, * Highly Competitive Salary: We are not saying our salary is a blank check, but let's just say it won't be a source of your stress
- Share Option Scheme: We are all in this together! We believe in shared success while we build the Lumai of tomorrow
- Pension Scheme: Plan for retirement with AVIVA
- Private Health Insurance: We firmly believe that you come first, and a happy you is a healthy you! Look after yourself and your loved ones with AXA
- Cycle to Work: Spread the cost of a bike, a bike and accessories or just accessories and save on tax
- L&D Allowance: Stay at the forefront of your field with a £500 annual development budget
- Subsidised On-site Lunches: Enjoy on-site healthy meals at half the price, as Lumai covers 50% of the cost
- Holidays: Enjoy some deserved "me time" with 25 days paid holiday (plus bank holidays) per year
- Socials: Be part of an inclusive community enjoying occasional all-company off-sites, lunches and socials