AI Field Engineer

Location: Can be based out of San Mateo, CA or NYC.

We re partnering with a highgrowth, latestage 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 customerfacing, handson 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

Latestage AI infra company with recent major funding and strong conviction from toptier investors; wellcapitalized and scaling quickly.

Remotefriendly across the US, with hubs on both coasts (San Mateo, CA or NYC) and regular travel to marquee enterprise customers.

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 endtoend 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 finetuning 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 Google Cloud Platform) 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.