It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
Director, Software Engineering Compute 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
Director, Software Engineering Compute 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.
- 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...
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