AI Field Engineer, AI infrastructure (Remote

📋 Job Overview

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. 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.

🏢 About Lavendo

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.

🎯 The Role

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.

✅ Key Responsibilities

• 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

📌 Required Qualifications

• 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

⭐ Desirable Experience

• 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.

🎁 Benefits

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.

🛂 Visa & Eligibility

Open to visa transfers (e.g. OPT, H1B transfers). Open to visa sponsorships (e.g. new H1B, TN).