Parasail is a cloud computing provider that supplies specialized inference infrastructure for companies running generative AI models.
LEAD CUSTOMER SUPPORT ENGINEER @ PARASAIL ABOUT PARASAIL Parasail is building the AI Supercloud, a programmable deployment network that gives AI builders production ready inference endpoints in minutes, scaling to massive traffic without contracts, infrastructure management, or vendor lock in. By aggregating global GPU supply and automating inference optimization, we let companies deploy custom AI models against aggressive latency, throughput, and cost targets, all on simple pay per token economics. We're one of the fastest moving companies in the second wave of inference providers. Since launching in April 2025, Parasail has grown to process over 500 billion tokens per day at roughly 30% month over month revenue growth, powering AI native teams like Elicit, mem0, Gravity, Kotoba, and Venice. In April 2026 we closed a $32M Series A (co led by Touring Capital and Kindred Ventures, with Samsung NEXT, Flume Ventures, and Banyan Ventures), bringing total funding to $42M. We're using that capital to deepen our orchestration and inference optimization, expand our global compute fabric, and strengthen partnerships across the GPU and data center ecosystem. It's an inflection moment. Demand for developer controlled AI is exploding, and the customers we support are running some of the most demanding inference workloads in the world. We're looking for engineers who want to sit at that frontier. THE ROLE We want engineers with real depth in AI/ML and low level systems who see customer support as a product to be engineered, not a queue to be cleared. Think of this less as a support role and more as an engineering seat positioned right at the customer's side. We're hiring problem solvers, not ticket closers. The issues that reach you are genuinely hard, because they come from production AI workloads operating at the edge of what's possible. You'll sit on the engineering team and commit production code next to the people building the core platform. The one twist is that your priorities are drawn from what the frontier of customer reality teaches you. In this role you'll: - Write code that compounds. Land bug fixes, new capabilities, and automation that lift the experience for every Parasail customer, not only the person who flagged the problem. - Partner with customers directly. Jump into Slack, email, and live calls to help developers and ML engineers troubleshoot, tune, and design their inference workloads. - Engineers support that scale. Build the dashboards, internal tools, and automated workflows that let a small team deliver great support to a fast growing customer base, and show up sharply at the moments that matter most. - Turn signal into a roadmap. Distill recurring field patterns into specific changes: documentation fixes, API improvements, and proposals for new features. WHAT WE'RE LOOKING FOR - 3+ years of experience in a customer facing technical role. - A real area of strength. You go deep in either low level infrastructure or ML/AI, and you can hold your own in the other. - Systems fundamentals. Comfort across operating systems, file systems, networking, performance profiling, distributed systems, and cluster management. - Hands on AI/ML. You've trained models, squeezed performance out of inference, worked directly with GPUs, or built ML infrastructure yourself. - A bias toward automating. When you hit a manual, repetitive process, your reflex is to engineer it away, and you have the chops to actually do it. - Strong communication. You can walk a customer through a thorny systems issue, file a tight reproducible bug report, and write documentation that lands, all while coordinating internally to get the fix out the door. NICE TO HAVE - Experience supporting or operating a developer facing platform or API. - A track record of customer facing technical work: solutions engineering, forward deployed engineering, or similar. - Familiarity with the modern inference stack (vLLM, TensorRT LLM, CUDA, Kubernetes, or comparable tooling).