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.