AI Field Engineer, AI infrastructure (Remote - US)

Lavendo partners with startups and high‑growth companies to help them hire top‑tier sales, GTM, and technical talent. This role is with one of our clients; we’ll share full details about the company and interview process as we get to know you and confirm mutual fit.

They believe the next generation of great products will be built by companies that own their AI stack, not just rent it. Their mission is making open-model AI infrastructure genuinely fast, flexible, and production-grade for the teams building at the frontier.

This is a forward-deployed engineering role where you're never far from either a terminal or a whiteboard with a VP on it. You'll work directly with VP Engineering, Head of AI/ML, and CTO-level partners at Fortune 500 enterprises and cutting-edge AI-native startups, owning the technical win from the first discovery call all the way through production. Two tracks are open — Enterprise and AI-Native — same role, different customer base, and we'll talk through which fits your background and interests best.

• Remote-friendly with hubs in New York and San Mateo — flexibility to work from anywhere in the US with regular travel to enterprise customers

• Run the full pre-sales field cycle yourself: discovery, POC scoping, model evals, and final model selection
• Ship real production code inside customer environments — this is hands-on building, not advisory slides
• Deploy and fine-tune open-model LLMs using frameworks like vLLM, SGLang, and TensorRT-LLM (SFT baseline, DPO/RFT a big plus)
• Act as a true partner to your sales counterpart, shaping deal strategy rather than just supporting it
• Build relationships across a customer's org, from engineers to executives, and know how to move a deal forward on both levels

• 3+ years in a client-facing AI/ML role — Solutions Architect, Forward-Deployed Engineer, Customer Success Engineer, Applied AI Engineer, Sales Engineer, or an AI/ML-focused SWE with genuine customer exposure
• Real hands-on experience with open-model LLM inference and/or fine-tuning
• Hyperscaler experience in an AI context (AWS, Azure, or GCP)
• Strong Python skills, comfort with GPU infrastructure and Kubernetes
• A background at an AI-native company or a SaaS company genuinely building AI features (not bolting them on)
• Full-time work history, and openness to travel to enterprise customers as needed

Key Success Drivers

You're the rare person equally comfortable debugging inference trade-offs with an ML engineer in the morning and presenting the same trade-offs to a VP by afternoon. You bring genuine customer obsession alongside technical depth — this role rewards people who've shipped things themselves, not just advised on them. Strong candidates own outcomes end-to-end and know how to build trust across both engineering and executive stakeholders.

• Base salary $176K–$224K, OTE $220K–$280K
• Performance-based variable compensation, paid quarterly
• Meaningful equity in a fast-growing, well-funded startup — Series D at a $17.5B valuation

• Comprehensive benefits package

Interviewing Process

• Take-home assignment (self-paced, 24-hour window once scheduled): build a working text-to-SQL system and show it works
• Recruiter screen (30 min): logistics, motivation, fit
• Culture + live coding (1 hr): product discussion, then extend your take-home live
• Discovery + hiring manager (45 min): live discovery role-play
• On-site final loop (~2 hrs): customer demo/presentation plus an executive values conversation
• Executive interview (30 min)
• Offer

Our client is a fast-scaling AI infrastructure company that helps enterprises and AI-native startups build, fine-tune, and serve their own specialized AI models — no more relying on someone else's black-box API. Their platform runs in production for some of the most recognizable names in tech, spanning text, image, embedding, audio, and multimodal workloads. Freshly backed by a Series D at a $17.5B valuation, this is a company with real scale, real customers, and real momentum behind it.