AI Engineer

AI Engineer (Enterprise) We’re partnering with a high‑growth, late‑stage AI infrastructure company to hire multiple AI Field Engineers (Enterprise) who can sit at the intersection of deep generative AI engineering and complex enterprise customer work. This is a customer‑facing, hands‑on role where you’ll turn ambitious GenAI ideas into production systems for some of the most sophisticated organizations in the world. Why this role is compelling · Late‑stage AI infra company with recent major funding and strong conviction from top‑tier investors; well‑capitalized and scaling quickly. · OTE in the ~220K–280K range with meaningful equity in a ~200‑person business where ownership can still move the needle. · Remote‑friendly across the US, with hubs on both coasts and regular travel to marquee enterprise customers. · Urgent hiring need with a highly engaged hiring team, targeting multiple hires in this function over the near term. What you’ll be doing · Lead technical discovery with enterprise customers, scope POCs, and run load tests/evaluations to validate the right model architectures and deployment setups. · Build end‑to‑end POCs and production integrations directly inside customer environments, working through infra, security, and compliance constraints to get systems live. · Advise customers on model selection and fine‑tuning strategies (e.g., SFT, DPO, RFT) and design evaluation frameworks that get them from experimentation to production at scale. · Own the technical relationship across complex accounts — identify champions, handle detractors, and align stakeholders to keep deals and deployments moving. · Feed recurring patterns and customer pain points back into the product and engineering org as a direct loop from field to roadmap. What you’ve done · 3+ years in customer‑facing AI/ML or infrastructure roles (Field Engineer, Applied AI Engineer, Solutions Architect, ML Engineer, or similar) with a track record of owning technical workstreams in enterprise accounts. · Shipped real AI/ML production code into customer environments — not just slideware or advisory engagements. · Hands‑on experience with LLM inference and/or training using open‑model frameworks (for example, modern serving stacks and fine‑tuning workflows such as SFT; exposure to more advanced approaches like DPO or RFT is a strong plus). · Strong Python, plus comfort with GPUs and cloud infrastructure (AWS, Azure, or GCP) and container/orchestration tools such as Kubernetes. · Demonstrated executive‑level presence: you can dive deep with an engineer and explain trade‑offs to senior leadership in the same day. What they’re not looking for · Profiles whose LLM experience is limited to closed‑model APIs and wrapper libraries without real exposure to open‑model inference or fine‑tuning. · Purely advisory or research‑only backgrounds without evidence of shipping production systems. · Pure Big Tech careers with little to no startup, field, or high‑velocity customer‑facing experience. Location, travel, and structure · US‑based, remote‑friendly role with the option to work from coastal hubs if desired. · Regular domestic travel to enterprise customers for discovery, POCs, and production rollout support. · Visa support available for select categories, including common transfer paths for experienced engineers.