# Software Engineer, Inference at Luma

Software Engineer, Inference at Luma in Remote, EU. Remote, full-time. Posted October 8, 2026.

- Company: Luma
- Location: Remote, EU
- Workplace: Remote
- Job type: Full-time
- Posted: 2026-10-08
- Apply: https://jobs.ashbyhq.com/lumaai/d0f68f86-1ec2-4443-a730-0d8f68842545/application
- Source: https://jobmagic.ai/jobs/software-engineer-inference-luma-pxq4r7

## Description

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 PyTorch, Hugging Face, vLLM, SGLang, TensorRT-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 (RoCE, InfiniBand, 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.*