Principal Engineer, Model Performance
Own and lead inference performance across Runwareβs model fleet, setting the roadmap and building production optimizations that improve latency, cost, and quality across GPU generations and workloads.
Own and lead inference performance across Runwareβs model fleet, setting the roadmap and building production optimizations that improve latency, cost, and quality across GPU generations and workloads.
Own the end-to-end network architecture for GPU data center fabrics, backbone and edge, and cloud connectivity, improving inference latency, reliability, and cost while establishing scalable operations and automation.
Own and build security controls for a distributed AI supercomputer, protecting bare-metal GPU infrastructure, Kubernetes clusters, customer workloads, and the control plane from firmware through incident response.
Lead the global infrastructure operations function, scaling and maintaining Runwareβs server, storage, and network fleet while building a high-performing team and improving reliability, deployment speed, and operational efficiency.
Own and build the end-to-end network architecture for a distributed AI supercomputer, from GPU fabrics and optical links to inter-site connectivity, while leading engineers and setting operational standards.
Own and build the storage architecture powering Runwareβs AI supercomputer, moving model weights and data across a global GPU fleet while improving load speed, reliability, and operational scale.
Leads and scales technical support across product lines by building the team, support models, automation, and knowledge systems while resolving complex issues and coordinating with Engineering, Product, Sales, and Customer Success.
Build and optimize automated go-to-market systems that improve customer acquisition, activation, conversion, expansion, and retention, connecting account, product, and revenue data across commercial teams.
Own the strategy, roadmap, and revenue outcomes for serverless and compute products, translating developer and infrastructure needs into reliable production workloads and driving adoption and growth.
Own the strategy, roadmap, and commercial outcomes for an LLM inference product, guiding model selection, API capabilities, production readiness, customer adoption, and enterprise growth.
Own positioning, messaging, and go-to-market for model APIs and compute products, translating technical capabilities into customer value and driving launches, sales enablement, and adoption.
Lead the design and delivery of scalable AI platform systems, spanning backend APIs, SDKs, developer tools, and inference orchestration, while improving performance, reliability, and developer experience.
Lead the design and delivery of scalable systems for an AI platform, spanning backend APIs, SDKs, developer tools, and inference orchestration while improving performance, reliability, and developer experience.
Build and evolve the backend systems and APIs powering the Web App and developer experience, owning work from requirements through delivery while improving reliability, security, and usability.
Technically lead and build a serverless platform for deploying models and running AI workloads, designing reliable distributed systems that simplify infrastructure and improve the developer experience.
Experiment with generative image and video models, build repeatable creative workflows, and turn technical discoveries into compelling examples, demos, and content for developers and creators.
Own the strategy, roadmap, and revenue outcomes for serverless GPU and compute products, translating developer and infrastructure customer needs into reliable production experiences and commercial growth.
Lead machine learning projects from research and experimentation through production deployment, optimizing multimodal model fine-tuning, inference performance, evaluation, and scalable serving systems.
Lead and grow engineering teams, owning people development, delivery, and technical direction while partnering across Product, DevOps, and ML to scale reliable, high-performing backend systems.
Own reliability, availability, and performance across production services by improving observability, resolving incidents, reducing operational toil, and strengthening distributed systems as the platform scales.
Build and operate the infrastructure powering real-time AI inference, scaling GPU fleets and production systems while improving performance, reliability, observability, security, and operational automation.
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