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
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
Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis
Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution
We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance
Our commitment
Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto
We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision
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