Staff AI-Native Platform Engineer (Forward-Deployed)

Doist

New York (NY)

On-site

USD 150,000 - 210,000

Full time

9 days ago

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Job summary

Upside is building an AI-native platform to support its go-to-market teams. You will be the first engineering hire with a focus on embedding with business units and shaping an end-to-end platform that drives value across merchants, partners, and revenue streams.

You will define the architecture, own critical technical direction, and push fast from prototype to production, while maintaining a strong bias toward real business outcomes and scalable security practices.

Qualifications

  • 6-8+ years of engineering experience in end-to-end systems.
  • Proven track record from zero to one: greenfield platforms.
  • Experience building with LLMs and AI APIs in production.
  • Ability to work with ambiguity and deliver outcomes.
  • Strong communication with technical and non-technical stakeholders.

Responsibilities

  • Build the first version of an AI-native platform for go-to-market teams.
  • Embed with revenue teams to learn workflows firsthand and shape systems.
  • Define architecture and technical direction for the platform.
  • Move from prototype to production quickly and iteratively.
  • Anchor work to business outcomes and measurable impact.
  • Collaborate with security and infrastructure teams to ensure reliability.
  • Document decisions and tradeoffs for future teams.

Skills

Staff-level engineer
LLM experience
Prompt design
Independent work
Cross-functional communication

Tools

Snowflake
Vector databases
RAG

Job description

Upside is building an AI-native platform to support its go-to-market teams. You will be the first engineering hire with a focus on embedding with business units and shaping an end-to-end platform that drives value across merchants, partners, and revenue streams.

You will define the architecture, own critical technical direction, and push fast from prototype to production, while maintaining a strong bias toward real business outcomes and scalable security practices.

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