Senior Site Reliability Engineer

About the role

AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there.

Responsibilities

• Scale a global GPU fleet.
• Build and improve the Kubernetes infrastructure behind provisioning, networking, storage, and service deployment across providers and regions.
• Make failure survivable.
• Design better isolation, failover, and recovery so hardware and infrastructure failures have less impact on customers.
• Build software that runs infrastructure.
• Automate capacity expansion, deployments, and maintenance, eliminating manual work and making changes safer.
• Make the system understandable.
• Develop observability and diagnostics that reveal bottlenecks, surface failures, and help engineers act quickly.
• Own the production feedback loop.
• Respond to incidents, get to the root cause, and turn what you learn into stronger systems.
• Push the platform forward.
• Work across the stack to improve performance, utilization, security, and reliability as inference demand grows.

Requirements

• Experience building and operating production infrastructure or distributed systems, with real ownership of reliability.
• Strong Linux fundamentals and practical knowledge of networking, storage, and containers.
• Hands-on experience running Kubernetes in production.
• The ability to write maintainable software and automation to solve infrastructure problems.
• A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries.
• Good judgment about when to move quickly, when to simplify, and where reliability matters most.
• The initiative to take a problem from investigation through implementation and work closely with teammates along the way.
• Your strongest skill might be software development, distributed systems, or infrastructure operations. We’re building a team with complementary strengths; your previous job title matters less than what you can build and own.

Skills

• Experience with multi-region, multi-provider, or bare-metal infrastructure.
• Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang.
• Experience with infrastructure as code, CI/CD, observability, or automated recovery.
• Experience building highly available services, multi-tenant platforms, or distributed data systems.

Benefits

• The systems you build will determine how reliably and efficiently customers can run AI in production.
• You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack.
• This is a small team tackling problems at substantial scale.
• You’ll own meaningful architecture decisions, ship improvements directly into production, and help build the foundation for the next stage of AI infrastructure.