Member of Technical Staff - Engineering Lead, Compute Platform

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.

- 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.

- 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.