Lead and hands-on build Luma's inference serving platform: architect scheduling, routing, autoscaling, and observability across thousands of GPUs; operate and optimize heterogeneous fleets; ship new model architectures from research to production; own SLOs, cost, incidents, and team hiring/mentorship.
The Role
What You’ll Do
Representative Projects
Background
Bonus Points
About Luma
Inference is where all of Luma’s compute meets all of Luma’s users. The inference platform team owns the entire serving stack — from request routing, scheduling, and queueing to fleet-wide orchestration across thousands of GPUs spanning multiple clusters, clouds, and hardware vendors. The team has a dual mandate: maximize the efficiency, reliability, and unit economics of production inference for millions of users, and enable research to move fast — new model architectures should go from research checkpoint to production in days, and our serving stack increasingly powers training itself through online reinforcement learning.
We are hiring a Tech Lead Manager to lead this team through its next phase of growth. This is a hands-on leadership role, not a pure management position: we expect you to spend at least 50% of your time as an individual contributor — designing, building, and debugging in the serving stack — alongside hiring and growing the team, setting technical direction, and partnering across research, product, and infrastructure. You lead by shipping, and you set the technical bar for the team through your own work.
- Spend at least half your time hands-on in the serving stack: architect and build core platform components, own the hardest design decisions, and debug the toughest production incidents yourself
- Lead, grow, and develop the inference engineering team: own hiring, coaching, and career growth, and build the team’s operational culture — on-call, incident response, capacity planning, and postmortems
- Set the technical roadmap for the serving platform: model serving engines, request routing and scheduling, autoscaling, caching, observability, and deployment pipelines
- Own the platform’s SLOs and economics: latency and availability targets, GPU utilization, and cost per generation across every model we serve
- Partner closely with research to ship new model architectures into production on day zero, and to integrate serving into online RL and evaluation loops
- Manage and optimize inference workloads across heterogeneous fleets — multiple clusters, clouds, and GPU vendors — including capacity planning and hardware bring-up
- Build sophisticated scheduling and queueing systems that optimally leverage expensive GPU resources against live traffic patterns, cluster availability, and user priority
- Design intelligent routing and scheduling that optimizes request distribution across thousands of GPUs in multiple regions and clouds
- Stand up disaggregated prefill/decode serving with tiered KV-cache reuse across GPU memory, DRAM, NVMe, and network storage
- Autoscale and hot-swap models across the fleet to dynamically match GPU supply with live demand across production, research, and experimental workloads
- Take a new multimodal architecture from research checkpoint to a production deployment serving millions of users, including quantization, speculative decoding, and precision/regression validation across hardware platforms
- Build end-to-end tracing that follows any inference request through its full lifetime — queueing, routing, prefill, decode, and delivery
- Integrate the inference stack into an online reinforcement learning pipeline where serving throughput directly gates training progress
- 8+ years of engineering experience 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 scale — you have operated fleets on the order of thousands of GPUs across multiple clusters or clouds, and you understand what breaks at that scale
- Technical leadership experience, including managing or leading engineers through periods of rapid growth — and a genuine desire to keep at least half your time in hands-on technical work rather than move into pure management
- Deep, practical expertise in LLM and foundation-model serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) — ideally you’ve modified engine internals, debugged edge cases under load, and contributed improvements back
- Strong command of the serving-performance toolkit: continuous batching, KV-cache management, quantization, speculative decoding, and parallelism strategies (TP/EP/pipeline)
- Strong Python and PyTorch; experience operating services on Kubernetes at scale
- Experience with queues, scheduling, traffic control, and fleet management at scale
- Experience serving diffusion, video, or other multimodal generative models (not just text), and with FFmpeg/multimedia processing
- Experience with modern networking stacks — RDMA (RoCE, InfiniBand), NVLink — including KV-cache transfer and multi-node serving topologies
- Experience across heterogeneous accelerator platforms (NVIDIA, AMD, TPU, Trainium) and the porting/validation work 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
Luma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
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. So, 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.
Similar Jobs
.png){kind=logo}
AdTech • Cloud • Digital Media • Information Technology • News + Entertainment • App development
Leads the vision, strategy, roadmap, and lifecycle of NBCUniversal’s mobile product experience. Partners with design, engineering, production, analytics, and business teams to define priorities, requirements, metrics, launches, and improvements. Focuses on onboarding, engagement, retention, monetization, experimentation, and long-term product health while communicating tradeoffs and building the mobile product organization.
Top Skills:
ConsoleCross-PlatformMobilePc
AdTech • Cloud • Digital Media • Information Technology • News + Entertainment • App development
Leads production strategy and delivery planning for an AAA game product pillar. Owns milestone and gate commitments, coordinates pod plans and cross-pillar dependencies, tracks risks and delivery confidence, represents the pillar in executive reviews, evaluates scope changes, and improves planning and delivery practices. Partners with product, design, craft, and production leadership to ensure integrated, achievable outcomes.
Top Skills:
Ai TechnologiesJIRA
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Design, build, and maintain customer-facing React frontend systems for search, catalog navigation, data lineage, dashboards, and AI-assisted analysis. Improve performance across large trees, tables, and streaming interfaces; shape product and UX decisions with design and product partners; raise engineering quality through reviews, documentation, complex bug resolution, and mentorship.
Top Skills:
Atlassian Design SystemCypressJavaScriptJestPlaywrightReactTypescript
What you need to know about the Charlotte Tech Scene
Ranked among the hottest tech cities in 2024 by CompTIA, Charlotte is quickly cementing its place as a major U.S. tech hub. Home to more than 90,000 tech workers, the city’s ecosystem is primed for continued growth, fueled by billions in annual funding from heavyweights like Microsoft and RevTech Labs, which has created thousands of fintech jobs and made the city a go-to for tech pros looking for their next big opportunity.
Key Facts About Charlotte Tech
- Number of Tech Workers: 90,859; 6.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lowe’s, Bank of America, TIAA, Microsoft, Honeywell
- Key Industries: Fintech, artificial intelligence, cybersecurity, cloud computing, e-commerce
- Funding Landscape: $3.1 billion in venture capital funding in 2024 (CED)
- Notable Investors: Microsoft, Google, Falfurrias Management Partners, RevTech Labs Foundation
- Research Centers and Universities: University of North Carolina at Charlotte, Northeastern University, North Carolina Research Campus
