Senior Sales Engineer

Senior Sales Engineer @ 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

Sales Engineers are how Parasail earns the trust of the teams building on us. You'll be benchmarking inference, reasoning about latency and cost tradeoffs, and writing real code to get a customer unblocked, not just narrating slides. It's also deeply human: the same week you might pair with a founder's lead engineer in a shared Slack channel and walk a VP through why our numbers hold up. What you learn in those rooms is some of the clearest signal our product team gets, and we expect you to carry it back.

A typical engagement runs through your hands end to end:

Understand the problem. Run deep discovery to fully understand the customer's requirements and use case, what they've optimized for in the past, where they're investing today, and what "good" actually looks like to them, before you propose anything.

Prove it on Parasail. Scope a focused proof of concept, agree with the customer on how success will be measured, and drive it to a working result alongside our product and engineering teams. You keep the scope defined and the timeline real.

Tune for production. Run inference sweeps, set performance baselines for their workload, and dial in deployments that hit their specific latency, throughput, and cost numbers. Recommend the right model and serving approach, and help them stand up the evals that keep quality honest once they scale.

Stay their technical partner. Keep the relationship healthy from the first engineer who championed you to the executive who signs off, sustain momentum through longer cycles, and turn what you see in the field, the recurring friction, the missing capability, the pattern across accounts, into concrete input for our roadmap.

What We're Looking For

• A few years of technical, customer facing experience. Roughly 4+ years where you've been the technical owner of customer relationships, in solutions or sales engineering, forward deployed engineering, applied AI, or a comparable hands on role.

• Real fluency with inference. You understand serving models at scale and the tradeoffs that come with it, and you can speak credibly about latency, throughput, and cost without hand waving.

• You write code. Python specifically, at a level where you can build, read, and debug something that runs in production, not only prototype it.

• Cloud at scale. Practical experience operating on AWS, Azure, or GCP and serving real workloads on them.

• A communicator people trust. You can lead a focused discovery call, hold your own in front of an executive, and get into the weeds with an engineer, adjusting your altitude to the room.

Nice to Have

• Hands on time with modern inference tooling such as vLLM, TensorRT LLM, CUDA, or Kubernetes.

• A working sense of when and how to fine tune, and the methods behind it.

• Experience moving fluidly across engineering, product, and go to market without dropping the thread.