Senior Software Engineer, Data Infrastructure Compute

About the role

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

Roblox’s data platform processes petabytes of data daily, powering analytics, ML, and product decisions. The Data Platform Compute team owns the compute layer underneath it: the Kubernetes (EKS) clusters where all of Roblox’s Spark workloads run, and the software that lets engineers, analysts, and ML practitioners submit and run that work reliably and cost-effectively.

As a Senior Software Engineer on this team, you will build and operate the systems that sit between our users and the cluster. These include the Spark job submission service, our internal Spark libraries, interactive notebook compute, and cluster lifecycle automation. You’ll have real ownership, you’ll work on problems where the design is not yet settled, and you’ll help decide which technologies Roblox adopts next.

You Will:

- Build and own core compute platform services: Design, build, and operate the software behind Spark job submission, including APIs, scheduling integration, and the libraries users depend on.

- Launch interactive Spark at scale: Deliver Spark Connect to support notebook workflows, including provisioning and managing the EKS compute that backs them, with sensible isolation, resource limits, and cost controls.

- Run a safe, automated cluster fleet: Build fleet management automation for EKS, such as blue-green rollouts that validate new AMIs and Kubernetes versions before they reach production workloads, with minimal disruption to running jobs.

- Make jobs run better without user effort: Develop our auto-tuner, which analyzes Spark history and event logs to recommend or apply job configuration changes that improve performance and reduce cost.

- Own batch scheduling on Kubernetes: Operate and upgrade our YuniKorn-based scheduling, including queue design, quotas, fairness across tenants, and version upgrades.

- Shape what we adopt next: Evaluate emerging technology such as native Spark acceleration (e.g., Apache Comet). Run rigorous benchmarks on real Roblox workloads and make a clear recommendation backed by data.

- Be an owner of reliability and cost: Define SLOs and metrics, build observability, and drive down toil, incidents, and compute spend across a multi-tenant platform.