Director, Compute Engineering

Director, Compute Engineering will lead the engineering organization that builds and operates our compute platform. You will evolve it into a scalable, efficient, and AI-driven compute model spanning private and public cloud, covering scheduling, capacity management, and performance engineering. What you get to do in this role: • Lead the compute engineering organization, providing technical leadership, mentoring, and career development for managers and engineers. • Own the roadmap for the compute scheduler, including placement, bin-packing, prioritization, preemption, and fairness across workloads. • Define and drive the compute-at-scale model, including unit economics such as cost per workload, utilization, and capacity headroom. • Lead capacity management for private and public cloud: demand forecasting, reservations, procurement, and cross-cloud placement. • Establish and scale a performance engineering practice: benchmarking, load and scale testing, regression detection, and SLOs for scheduling latency and throughput. • Apply AI and ML to demand forecasting, anomaly detection, and placement optimization. Set the AI/ML roadmap and decide where it outperforms heuristics. • Work with product management, SRE, FinOps, and infrastructure partners to set priorities and tie capacity decisions to cost and customer outcomes. • Drive engineering excellence through architecture reviews, design standards, and modern software and DevOps practices. • Anticipate technical and organizational risks that affect multiple products or platforms, and provide clear, structured mitigations. • Assess availability, scalability, and operational risk across compute systems, and lead the optimization work that follows. • Manage resources, staffing, and succession planning to build and retain a best-in-class engineering organization. • Share best practices and engineering standards with other teams across the organization. • Define success measures for the first 12 to 18 months, such as utilization gains, scheduler latency targets, forecast accuracy, and cost per unit. To be successful in this role you have:   • Experience in leveraging or critical thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. • 15+ years of software engineering experience with a Bachelor's degree; or 12 years with a Master's degree; or a PhD with 8 years of experience; or equivalent experience. • 5+ years of people management experience, ideally leading software or systems engineering teams in a global 24x7 production environment. • Experience building or operating large-scale schedulers or resource managers, such as Kubernetes, Slurm, or internal equivalents. • Deep expertise in distributed systems, system design, and capacity planning at scale. • Hands-on experience applying ML in production, including models, feature pipelines, or ML platforms. Using AI tools alone is not enough. • Experience running performance engineering or capacity management across hybrid or multi-cloud estates, including private cloud and public cloud platforms. • Fluency in multiple programming languages (such as Java, Python, or Go) and strong system design judgment. • Experience with CI/CD, DevOps, configuration management at scale, and automation tools. • Deep background in Linux systems architecture and engineering on a global scale. • Working knowledge of infrastructure components, including hardware platforms, operating systems, virtualization, cloud platforms, and web applications and databases. • Financial acumen on cloud and infrastructure spend, including cost modeling and Fin