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

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  Late-stage AI infra company with recent major funding and strong conviction from top-tier investors; well-capitalized and scaling quickly.
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  OTE in the ~220K-280K range with meaningful equity in a ~200-person business where ownership can still move the needle.
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  Remote-friendly across the US, with hubs on both coasts and regular travel to marquee enterprise customers.
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  Urgent hiring need with a highly engaged hiring team, targeting multiple hires in this function over the near term.

What you'll be doing

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  Lead technical discovery with enterprise customers, scope POCs, and run load tests/evaluations to validate the right model architectures and deployment setups.
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  Build end-to-end POCs and production integrations directly inside customer environments, working through infra, security, and compliance constraints to get systems live.
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  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.
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  Own the technical relationship across complex accounts - identify champions, handle detractors, and align stakeholders to keep deals and deployments moving.
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  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

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  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.
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  Shipped real AI/ML production code into customer environments - not just slideware or advisory engagements.
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  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).
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  Strong Python, plus comfort with GPUs and cloud infrastructure (AWS, Azure, or GCP) and container/orchestration tools such as Kubernetes.
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  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

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  Profiles whose LLM experience is limited to closed-model APIs and wrapper libraries without real exposure to open-model inference or fine-tuning.
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  Purely advisory or research-only backgrounds without evidence of shipping production systems.
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  Pure Big Tech careers with little to no startup, field, or high-velocity customer-facing experience.

Location, travel, and structure

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  US-based, remote-friendly role with the option to work from coastal hubs if desired.
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  Regular domestic travel to enterprise customers for discovery, POCs, and production rollout support.
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  Visa support available for select categories, including common transfer paths for experienced engineers.