About the role
Our Mission Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
ABOUT
THE ROLE Reflection's Compute Platform team keeps our compute layer healthy and highly available. We run a Kubernetes-based platform distributed across multiple neo-clouds, where multi-cloud scheduling, node health, and performance debugging at scale present genuinely hard systems problems. As Compute Platform Lead, you'll provide front-line leadership of
the team that builds and operates this layer. You'll build, mentor, and grow a team of strong systems engineers, guide the technical and architectural decisions across multi-cloud scheduling, cluster management, and next-generation GPU deployments, and work closely with our training teams to co-design fault tolerance, node health checks, and remediation. You'll stay close enough to the systems to make targeted contributions as an individual contributor and to maintain a deep understanding of the compute fleet our largest training runs depend on. Managing vendors — and the important deals that come with them — is a core part of the job.
WHAT YOU'LL DO Build, mentor, and grow a high-performing team of systems engineers. Coach and support your reports in understanding, and pursuing, their professional growth. Provide front-line leadership of engineering efforts to keep the compute fleet reliable and highly available — multi-cloud scheduling, cluster management, and the path to next-generation GPUs and increasingly larger cluster sizes. Stay hands-on: become familiar with
the team's technical stack enough to make targeted contributions as an individual contributor. Manage day-to-day execution: prioritize
the team's work and manage projects in a highly dynamic, fast-paced environment. Guide technical and architectural decisions, emphasizing scalability, robustness, and reliability — automatic remediation, topology-aware scheduling, capacity planning, rapid hardware debugging, and cluster-wide monitoring and performance benchmarking. Work closely with our training teams to co-design fault tolerance, node health checks, and remediation, and manage the vendor relationships and important deals the compute fleet depends on. Prepare the fleet for what's next: next-generation GPUs and larger clusters, and — longer term — multi-cloud storage, petabyte-scale data replication, and GPU-to-GPU network performance. Raise the bar for technical judgment, prioritization, communication, and execution in a fast-moving environment.