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 DaysOne 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.
