Reflection AI Engineering Lead for Reflection AI's Compute Platform leads a team managing a Kubernetes-based multi-cloud compute layer. The role includes technical leadership, mentoring, vendor management, and hands-on contributions.
Responsibilities
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.
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.
Qualification
Experience buildingDepth in at least one of orchestrationExperience managing vendors
Required
Experience building, mentoring, and growing systems or infrastructure teams while staying technically hands-on. (Comfortable growing into leading a team of ~10 quickly if you haven't managed at that scale before.)
Deep systems-level engineering experience with a focus on cluster-wide behavior and maintenance.
Strong coding ability and the credibility to earn the technical trust of a strong team.
Depth in at least one of orchestration, storage, or GPU hardware — with the ability to learn the rest. Deep GPU knowledge beyond standard Kubernetes (e.g., NCCL) is a plus, not a prerequisite.
Alignment with a Kubernetes-first architecture.
Cloud storage expertise — managing high-performance data products (like VAST) across multiple data centers and handling datasets and checkpointing at scale — is a plus.
Experience managing vendors, including negotiating and operating important deals.
Ability to guide strategy and drive execution across a multi-cloud, large-fleet environment, and to partner effectively with research and training teams.