At a Glance
- Tasks: Own and optimise Luma's model serving and deployment across thousands of machines.
- Company: Join Luma, a pioneering tech company on a mission for unified general intelligence.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with excellent career growth opportunities and a focus on collaboration.
- Why this job: Make a real impact in AI by working with cutting-edge technologies and innovative projects.
- Qualifications: Strong Python skills and experience with model deployment and Kubernetes.
The predicted salary is between 72000 - 88000 £ per year.
You’ll own how Luma’s models get served - integrating new architectures into the inference engine, scaling deployments across thousands of machines, and keeping expensive GPU fleets busy while meeting internal SLOs.
This is large-scale inference systems work: scheduling, fleet management, deployment pipelines, and reliability across clusters and hardware providers.
It fits a strong systems engineer comfortable with model serving and Kubernetes at scale.
If you want pure modeling rather than the systems that run models, this is firmly the systems side.
What You’ll Own Ship new model architectures by integrating them into the inference engine.
Collaborate across research, engineering, and infrastructure to optimize model efficiency and deployments.
Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows.
Automate, test, and maintain inference services for maximum uptime and reliability.
Manage and optimize inference workloads across clusters and hardware providers, and scale deployments across thousands of machines.
Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.
First 90 Days One way the first 90 could unfold.
Days 1-30 - Immerse & Diagnose: Learn the inference stack, the fleets, and where reliability or utilization break.
Days 30-60 - Ship & Validate: Integrate a model or ship tooling/scheduling that improves uptime or GPU utilization.
Days 60-90 - Scale & Systemize: Harden deployment pipelines and scheduling across clusters and providers.
What You Bring Strong Python and system-architecture skills.
Experience deploying models with Py Torch, Hugging Face, v LLM, SGLang, Tensor RT-LLM, or similar.
Experience with queues, scheduling, traffic control, and fleet management at scale.
Experience with Linux, Docker, and Kubernetes, and with orchestration, deployment, and scheduling.
Familiarity with Redis and S3-compatible storage.
Nice to Have Modern networking stacks including RDMA (Ro CE, Infini Band, NVLink).
High-performance large-scale ML systems (100+ GPUs).
CUDA, and FFmpeg or multimedia processing.
About
Luma: Luma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
We believe multimodality is critical for intelligence - the next step beyond language models comes from vision.
Luma is an equal opportunity employer.
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Software Engineer, Inference in London employer: Luma
At Luma AI, we pride ourselves on being an innovative employer that fosters a dynamic and collaborative work culture. As an Executive Assistant in the UK, you will play a pivotal role in shaping our operational strategies while enjoying opportunities for personal and professional growth in a fast-paced environment. With a commitment to expanding human imagination through cutting-edge technology, we offer a unique chance to make a meaningful impact alongside a passionate team dedicated to transforming the creative landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer, Inference in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Luma or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Luma.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Luma.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Luma that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Software Engineer, Inference in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Luma.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Luma and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Luma
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Luma uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
