Engineering Manager, Serverless Compute Platform
Engineering Manager owning end-to-end delivery and scaling of a new Execution Sandbox service powering non-Spark compute workloads across AWS, Azure, and GCP. Requires 5+ years managing engineers on distributed systems and deep infrastructure fluency.
Impact
- Own a 0→1 service with platform-wide blast radius. Architect and launch the Execution Sandbox Service from inception to production scale. This greenfield provisioning layer will power all non-Spark compute workloads on Serverless (Notebooks, AI Agents, Remote UDFs).
- Unify a fragmented compute surface. Converge disparate CPU and GPU cluster management paths into a single provisioning service, eliminating parity bugs and enabling consistent product experiences.
- Collaborate across 5+ partner organizations. Drive alignment on API contracts and shared milestones across Serverless Platform, AI Runtime, Lakeguard, and product teams.
- Shape product strategy through deep technical understanding. Partner with Product Management to leverage this new sandbox primitive for future offerings like serverless command execution APIs and FaaS-style workloads.
Responsibilities
- Own the end-to-end delivery of the Execution Sandbox service and the engineers building it.
- Build out the full vision, guide evolution, and scale the team.
- Ensure strong execution health and that the service launches with production-grade reliability spanning a range of use cases, e.g. GPU onboarding, UDF generalization, and managed REPL.
- Manage and elevate a team of strong L3-L5 engineers, establishing clear ownership boundaries and architectural doctrine.
- Hire 2-3 additional engineers to support this expanded scope.
Requirements
- 5+ years managing engineers building and operating distributed systems in production, ideally control-plane or orchestration services.
- BS or higher in Computer Science or a related field. Equivalent practical experience is equally valued.
- Deep technical fluency in infrastructure systems. Ability to deeply review architecture docs, challenge design tradeoffs (e.g., state machine design, API boundaries), and coach senior ICs.
- Experience with multi-cloud or multi-region service deployment (AWS, Azure, GCP).
- Bias toward operational rigor. Deep commitment to observability, SLOs, pre-mortems, and healthy on-call cultures.
- Build and scale a high-caliber team.
Senior Director, Engineering - Agentic Business Systems
Lead internal AI platform and agentic workflow deployment across business functions. Own infrastructure, ship high-impact automations, and manage a mixed engineering/product/business team reporting to the CEO.
Manager, Software Engineering, Search Discovery
Lead and grow a senior engineering team building dashboards, notebooks, visualizations, and AI-assisted investigation tools for Cribl Search. Partner with Product on roadmaps and mentor staff+ engineers in a fast-paced environment.
Engineering Manager
Engineering Manager for a Core Product team building AI-powered scheduling, payments, and client management systems. Owns execution, team health, and AI tooling adoption while partnering closely with Product and Design.
Engineering Manager
Engineering Manager for a Core Product team at GlossGenius, owning execution, quality, and team development while embedding AI tooling. Requires 5+ years engineering experience and 2+ years managing teams at high-growth tech companies.
Engineering Manager, Service Platform
Lead the Service Platform team building Instacart’s deployment, canary, and release engineering systems. Guide engineers to ship reliable code at scale while improving developer experience and platform tooling.