# Software Engineer, Inference - Company: [Lumaai]() - Location: Redwood City, CA - Remote: Yes - Team: Research & AI - Employment type: Full Time - Posted: July 24, 2026 ## Job 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.* ## Apply [Apply on Lumaai]() Canonical job page: