At a Glance
- Tasks: Develop and optimise cutting-edge AI models for real-world applications.
- Company: Join Luma AI, a leader in multimodal AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting projects and career advancement potential.
- Why this job: Be part of a mission to expand human imagination through advanced AI technology.
- Qualifications: Strong Python skills and experience with model deployment in AI frameworks.
The predicted salary is between 60000 - 80000 £ per year.
About Luma AI
Luma's mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. We are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
Responsibilities
- Ship new model architectures by integrating them into our inference engine
- Collaborate closely across research, engineering and infrastructure to streamline and optimize model efficiency and deployments
- Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows
- Automate, test and maintain our inference services to ensure maximum uptime and reliability
- Optimize deployment workflows to scale across thousands of machines
- Manage and optimize our inference workloads across different clusters and hardware providers
- Build sophisticated scheduling systems to optimally leverage our expensive GPU resources while meeting internal SLOs
- Build and maintain CI/CD pipelines for processing/optimizing model checkpoints, platform components, and SDKs for internal teams to integrate into our products and internal tooling
Qualifications
- Strong Python and system architecture skills
- Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar
- Experience with queues, scheduling, traffic‑control, fleet management at scale
- Experience with Linux, Docker, and Kubernetes
Bonus points:
- Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink)
- Experience with high performance large scale ML systems (>100 GPUs)
- Experience with FFmpeg and multimedia processing
Example Projects
- Create a resilient artifact store that manages all checkpoints across multiple versions of multiple models
- Enable hotswapping of models for our GPU workers based on live traffic patterns
- Build a robust queueing system for our jobs that takes into account cluster availability and user priority
- Architect an end‑to‑end model serving deployment pipeline for a custom vendor
- Integrate our inference stack into an online reinforcement learning pipeline
We think you need these skills to ace Software Engineer, Inference
Python
System Architecture
Model Deployment
PyTorch
Huggingface
vLLM
SGLang
