# Tech Lead Manager, Inference - Company: [Lumaai]() - Location: Redwood City, CA - Remote: Yes - Team: Research & AI - Employment type: Full Time - Posted: July 24, 2026 ## Job description You'll lead the team that owns Luma's entire inference serving stack — routing, scheduling, and fleet-wide orchestration across thousands of GPUs, multiple clouds, and hardware vendors — where all of Luma's compute meets all of its users. This is a hands-on tech-lead-manager role. It's leadership by shipping: at least half your time stays hands-on in the serving stack, alongside hiring, growing the team, and setting technical direction. It fits someone who's operated inference fleets at the thousands-of-GPUs scale and genuinely wants to keep building, not move into pure management. If you want a hands-off management seat, this isn't it. **What You'll Own** * Spend at least half your time hands-on: architect and build core platform components, own the hardest design decisions, and debug the toughest incidents yourself. * Lead, grow, and develop the inference engineering team — hiring, coaching, on-call, incident response, capacity planning, and postmortems. * Set the technical roadmap for serving: engines, routing, scheduling, autoscaling, caching, observability, and deployment. * Own the platform's SLOs and economics: latency, availability, GPU utilization, and cost per generation. * Partner with research to ship new architectures to production on day zero and integrate serving into online RL and evaluation loops. * Build scheduling and queueing that leverages expensive GPU resources against live traffic, cluster availability, and user priority. **First 90 Days** *One way the first 90 could unfold.* * **Days 1–30 — Immerse & Diagnose:** Learn the serving stack, the team, and where reliability, latency, or cost hurt most. * **Days 30–60 — Ship & Validate:** Personally ship a meaningful platform improvement while setting the team's technical bar. * **Days 60–90 — Scale & Systemize:** Set the roadmap, grow the team, and harden SLOs and economics across the fleet. **What You Bring** * 8+ years in large-scale distributed systems or ML infrastructure, with several years building and operating model-serving or inference platforms in production. * Experience running inference platforms at the thousands-of-GPUs scale across multiple clusters or clouds, and knowing what breaks there. * Technical leadership experience through rapid growth, with a genuine desire to stay at least half hands-on. * Deep expertise in LLM and foundation-model serving engines (vLLM, SGLang, TensorRT-LLM), ideally having modified engine internals. * Strong command of continuous batching, KV-cache management, quantization, speculative decoding, and parallelism strategies (TP/EP/pipeline). * Strong Python and PyTorch, Kubernetes at scale, and experience with queues, scheduling, traffic control, and fleet management. **Nice to Have** * Experience serving diffusion, video, or other multimodal generative models, and with FFmpeg/multimedia processing. * Modern networking stacks — RDMA (RoCE, InfiniBand), NVLink — and multi-node serving topologies. * Experience across heterogeneous accelerators (NVIDIA, AMD, TPU, Trainium) and the porting and validation that comes with them. * Contributions to open-source serving infrastructure (vLLM, SGLang, Ray, Kubernetes ecosystem). * Systems-language depth (Rust, C++, CUDA/HIP) for kernel- and runtime-level optimization. *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: