AI Field Engineer – Enterprise Full-Time | Remote (U.S.) Compensation: $176,000 – $224,000 Base On-Target Earnings (OTE): $220,000 – $280,000 + Meaningful Equity About the Role We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with ambitious enterprise customers and turn complex GenAI challenges into production systems—fast. You'll combine deep hands-on engineering with the executive presence to earn trust across large organizations and help drive engagements from initial technical discovery through production deployment. What You'll Do • Lead technical discovery calls, scope proof-of-concepts, and run load tests and evaluations to validate the right model architecture and deployment configuration for enterprise customers. • Build end-to-end POCs and production integrations directly inside customer environments while navigating infrastructure, security requirements, and organizational constraints. • Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks to move from experimentation to production at scale. • Manage relationships across multiple enterprise stakeholders, identifying technical champions and helping align teams to move projects forward efficiently. • Provide recurring customer feedback and deployment insights to the engineering organization to help shape future product development. Required Qualifications • Deep hands-on experience with LLM inference and/or training. • Working knowledge of open-model frameworks such as: • vLLM • SGLang • TensorRT-LLM • Experience with fine-tuning workflows: • SFT required • DPO and/or RFT strongly preferred • Candidates whose experience is limited to closed-model API integrations alone will not be a fit. • Proven ability to build and deploy production code within customer environments, including POCs or MVPs running in production. • Strong Python programming skills. • Experience with GPU infrastructure. • Experience using AWS, Azure, or GCP. • Experience with Kubernetes. • Strong communication skills with the ability to engage both technical and executive audiences. • Customer-facing engineering experience in roles such as: • Field Engineer • Applied AI Engineer • Solutions Architect • AI Infrastructure Engineer • ML Engineer • Software Engineer with pre-sales exposure • 3+ years of relevant experience. Preferred Qualifications • Experience with DPO or reinforcement fine-tuning (RFT). • Experience deploying enterprise-scale GenAI applications. • Experience working directly with production AI infrastructure. • Interest in contributing to product direction based on customer feedback. • Comfortable working in fast-paced, high-growth environments with significant ownership. Technical Environment • Python • vLLM • SGLang • TensorRT-LLM • Kubernetes • AWS • Azure • GCP • Azure AI Foundry • AWS Bedrock • AWS SageMaker • GCP Vertex AI • GPU Infrastructure • Open-source LLM Frameworks • LLM Fine-Tuning (SFT, DPO, RFT) Compensation & Benefits • Base Salary: $176,000 – $224,000 • On-Target Earnings: $220,000 – $280,000 (80/20 base/variable split) • Quarterly variable compensation based on individual and team performance • Compensation may exceed the posted range for candidates with extensive experience. • Meaningful equity package. Visa Sponsorship Visa sponsorship is available. • H-1B transfers • TN visas • O-1 visas considered on a case-by-case basis Location Remote (United States) Employees located near company hubs may follow a hybrid schedule. Regular travel to enterprise customer sites is expected. About the Company This fast-growing AI infrastructure company helps organizations build, fine-tune, and scale production AI applications using open-source models. Its platform is designed to deliver high-performance inference, lower latency, and greater scalability for enterprise AI workloads. Founded in 2021, the company has grown to approximately 211 employees and has raised over $327 million in funding. The engineering culture emphasizes technical excellence, ownership, rapid execution, and close collaboration between customer-facing teams and product engineering. The organization values engineers who enjoy solving complex technical challenges, working directly with customers, and helping shape the future of AI infrastructure.