Senior AI Field Engineer (Enterprise)

Job Description This is a remote position. Senior AI Field Engineer (Enterprise) About the Opportunity We are partnering with a high-growth, late-stage AI infrastructure company that recently secured major funding from top-tier venture capital firms. Well-capitalized and scaling rapidly, this ~200-person organization is expanding its technical footprint globally. We are seeking multiple AI Field Engineers to sit at the intersection of deep generative AI engineering and complex enterprise customer environments. This is a highly visible, hands-on role where you will turn ambitious GenAI concepts into production-grade systems for some of the most sophisticated organizations in the world. What You’ll Do Lead Technical Engagements: Drive technical discovery, scope POCs, and execute rigorous load testing/evaluations to validate model architectures and deployment setups. Build & Deploy: Develop end-to-end POCs and production integrations directly within client infrastructure, navigating complex security, compliance, and networking constraints. Strategic Advisory: Guide enterprise engineering teams on model selection, fine-tuning methodologies ( , , ), and robust evaluation frameworks to move systems from experimentation to planet-scale production. Own Account Architecture: Act as the trusted technical anchor across complex accounts, aligning diverse stakeholders and clearing technical bottlenecks. Product Feedback Loop: Champion the customer voice, feeding recurring technical patterns and infrastructure pain points directly back to core product and engineering teams to shape the roadmap. Requirements What You Bring 3+ Years of Proven Impact: Experience in customer-facing AI/ML or heavy infrastructure roles (e.g., Field Engineer, Applied AI Engineer, Solutions Architect, or MLOps Engineer) managing enterprise-grade technical workstreams. Production Credentials: A clear track record of shipping real AI/ML production code into live customer environments—not just building slideware, proofs of concept, or advisory frameworks. Open-Source LLM Expertise: Hands-on experience with LLM inference and/or training using open-model frameworks, modern serving stacks, and fine-tuning workflows (e.g., ). Familiarity with advanced alignment techniques like or is highly preferred. Core Technical Stack: Advanced Python proficiency, direct comfort optimizing GPUs, deep cloud infrastructure experience (AWS, Azure, or GCP), and container orchestration via Kubernetes. Executive Presence: The unique capability to dive deep into a technical codebase with engineers, and seamlessly transition to whiteboarding high-level architectural trade-offs for senior leadership later that afternoon. The Ideal Culture Match This environment moves exceptionally fast. The team values high-velocity execution and a "wearer of many hats" startup mindset. Careers spent exclusively within rigid, highly siloed Big Tech environments without high-velocity, customer-facing field exposure typically do not align with the pace of this role. Benefits Total Target Compensation: OTE in the $220K – $280K range, complemented by a meaningful equity stake in a fast-growing, venture-backed business. Location Flexibility: Remote-friendly across the United States, with established hubs on both coasts. Exceptional candidates will be considered for full remote arrangements. Support: Visa transfers and support available for select categories. Travel: Regular domestic travel to marquee enterprise customers for on-site discovery, architecture workshops, and production rollout support.