Senior AI Compute Infrastructure Engineer
6.0/10
Kraken
Not specified
Remote
senior
5 days ago
aicryptotechGPU computeML infrastructuredistributed systemshigh-performance computingLinuxPythonKubernetescontainersobservability
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Description
What you'll do
- •Own and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation.
- •Design infrastructure that enables Kraken teams to run models locally on GPUs.
- •Build and improve scheduling, orchestration, placement, quota management, and utilization systems.
- •Optimize inference pipelines for latency, throughput, reliability, memory efficiency, and cost.
- •Partner with ML engineers and researchers to remove bottlenecks in workflows.
- •Build observability for GPU utilization, memory pressure, and other metrics.
- •Drive reliability, incident response, alerting, and post-incident improvements.
- •Evaluate and integrate new hardware and cloud instance families.
- •Build tooling that makes GPU usage visible and easier for internal teams.
Requirements
- •5+ years of infrastructure engineering experience, with significant time spent on GPU compute and ML infrastructure.
- •Hands-on experience operating GPU clusters or accelerator-backed infrastructure.
- •Strong systems engineering fundamentals across Linux, networking, storage, and containers.
- •Experience with ML serving frameworks such as vLLM, Triton Inference Server, or equivalent.
- •Proficiency in Python for infrastructure automation and operational workflows.
- •Practical understanding of performance tradeoffs across various metrics.
- •Track record of optimizing compute costs while maintaining performance and reliability.
- •Experience building observable systems with useful metrics and incident workflows.
- •Comfortable working in high-stakes, always-on environments.
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