Senior AI Compute Infrastructure Engineer

Responsibilities

• Own and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation, including drivers, runtimes, kernels, device plugins, node configuration, scheduling primitives, and workload isolation.
• Design infrastructure that enables Kraken teams to run models locally on GPUs where it is strategically and economically preferable, reducing unnecessary dependency on external providers and containing compute costs.
• Build and improve scheduling, orchestration, placement, quota management, and utilization systems across heterogeneous accelerator environments.
• Partner with ML engineers and researchers to remove bottlenecks in training, evaluation, batch inference, online inference, deployment, and production debugging workflows.
• Build observability for GPU utilization, memory pressure, queue depth, saturation, token throughput, request latency, failed workloads, capacity pressure, and spend.
• Drive reliability, incident response, alerting, runbooks, and post-incident improvements for always-on AI compute infrastructure.
• Build tooling that makes GPU usage visible, accountable, and easier for internal teams to consume without needing to become infrastructure experts.
• Contribute to long-term architecture decisions that balance performance, cost efficiency, scalability, operational simplicity, and production safety.

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