Senior AI Compute Infrastructure Engineer

Requirements

• 5+ years of infrastructure engineering experience, with significant time spent on GPU compute, ML infrastructure, distributed systems, high-performance computing, or large-scale production platforms
• Hands-on experience operating GPU clusters or accelerator-backed infrastructure in production or production-like environments, including scheduling, orchestration, utilization monitoring, and cost optimization
• Strong systems engineering fundamentals across Linux, networking, storage, containers, Kubernetes, distributed runtimes, and production debugging
• Experience with ML serving frameworks such as vLLM, Triton Inference Server, TensorRT, TorchServe, KServe, Ray Serve, or equivalent systems
• Proficiency in Python for infrastructure automation, tooling, debugging, integration, and operational workflows
• Practical understanding of performance tradeoffs across batching, concurrency, memory usage, GPU utilization, model size, latency, throughput, availability, and cost
• Track record of optimizing compute costs while maintaining clear performance, reliability, and availability expectations
• Experience building observable systems with useful metrics, logs, traces, dashboards, alerts, and incident workflows
• Comfortable working in high-stakes, always-on environments where uptime, throughput, correctness, and operational discipline are critical
• Clear communicator who can translate infrastructure tradeoffs for researchers, product teams, platform engineers, security stakeholders, and engineering leadership

Nice to Haves

• Experience at a frontier AI lab, hyperscaler, high-frequency trading firm, research platform, or high-scale ML organization
• Familiarity with custom silicon or specialized accelerators such as TPUs, AWS Trainium, Gaudi, or similar platforms
• Background in capacity planning, procurement input, reserved capacity strategy, cloud accelerator economics, or GPU fleet cost management
• Experience with distributed training frameworks such as DeepSpeed, Megatron-LM, FSDP, Ray, or equivalent systems
• Experience debugging CUDA, NCCL, kernel, driver, runtime, memory, networking, or low-level performance issues
• Experience with Rust, C++, Go, CUDA, or other systems languages used for performance-critical infrastructure
• Crypto, financial services, trading infrastructure, or security-sensitive production infrastructure experience

What You'll Be Doing

• Own and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation
• Design infrastructure that enables running models locally on GPUs
• Build and improve scheduling, orchestration, and utilization systems
• Optimize inference pipelines for latency, throughput, and cost
• Partner with ML engineers to remove bottlenecks
• Build observability for GPU utilization and more
• Drive reliability and incident response
• Evaluate and integrate new hardware and cloud instance families
• Build tooling for GPU usage visibility
• Contribute to long-term architecture decisions

Perks and Benefits

• Global team with diverse talents and backgrounds
• Equal opportunity employer without discriminating based on various characteristics
• Celebration of all Krakenites for their unique perspectives
• Encouragement to apply even if not fully meeting requirements, especially if passionate about crypto
• Ongoing acceptance of applications
• Job-related skills or work-style assessments as part of the hiring process