Sales Engineering

ai is building the world's most reliable open-model inference platform.

ai is building the world's most reliable open-model inference platform.

Backed by AMD, Airbus Ventures, 500 Global, Kickstart Ventures, HF0 Residency, Panache Ventures, and Oakseed Ventures, we're a well-funded team of researchers and engineers on a mission to democratize AI through performance and accessibility.

Our cloud provides instant access to 40,000+ open-source AI models and the research innovations powering next-generation efficiency, reliability, and model optimization.

We're hiring our first Sales Engineer to pair directly with our founding Account Executive in North America. You are the technical half of a two-person deal team: the AE owns the commercial motion, you own the technical win.

That's your job — hands on keyboard, in the customer's stack, from first technical call through production rollout.

If you like being the person who can actually answer the hard question in the room — and want the technical foundation of a Series A GTM engine to be yours — this is for you.

What You'll Own Immediately

Own technical objection handling end-to-end: performance, reliability, cost modeling, security, and data handling

Be the field's voice into product and engineering — you'll know what we're losing on before anyone else does

Run benchmarks and produce the throughput, latency, and cost-per-token analysis that anchors the business case the AE builds

Scope and execute POCs with clear technical success criteria, then hold the customer and us to them

Feed structured field input to product and engineering — feature gaps, model coverage requests, reliability issues, competitive intel

Build and maintain the technical assets that scale the team: demo apps, notebooks, reference architectures, integration guides, internal enablement for BDRs and future AEs

Keep POC and technical-stage detail current in HubSpot so forecasting reflects technical reality, not optimism

3-6 years in pre-sales engineering, solutions architecture, or a forward-deployed / customer-facing engineering role — at a GPU cloud, inference provider, MLOps platform, AI/developer tooling company, or cloud infrastructure vendor

Genuinely hands-on: you write Python comfortably and build your own demos rather than requesting them

Working knowledge of modern LLM inference — serving stacks (vLLM, SGLang, TensorRT-LLM or similar), OpenAI-compatible APIs, quantization, LoRA and fine-tune serving, batching and KV cache behavior, and what actually drives tokens/sec and cost

Comfortable with containers, Kubernetes, and cloud networking and security fundamentals

Credible with both audiences in the same meeting — the ML engineer who wants the numbers and the CTO who wants the risk and cost story

Nice to have: experience selling or building on AMD GPUs / ROCm; open-source contributions or public technical writing; Build the sales engineering function at a company in one of the fastest-moving spaces in AI

Work as a true pair with the founding AE, and directly with the CRO and founders — small team, no layers, immediate impact

Real technical depth: 40,000+ open models and an in-house research team shipping inference and optimization work you'll get to sell

Competitive base + variable tied to the team's number, plus equity