Federal Frontline Engineer — AI Deployments & Strategy

Fdetimes

Washington (District of Columbia)

On-site

USD 180,000 - 260,000

Full time

14 days+

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Job summary

Cognition, an applied AI lab building end-to-end software agents, seeks a foundational engineer for its newly formed federal group in Washington, DC. You will shape technical direction, own deployment success, and influence product strategy across federal accounts while driving growth of a fast-growing, mission-driven team.

You will work across strategic accounts, own critical architecture decisions, and translate field learnings into product requirements, ensuring scalable, enterprise-grade

Qualifications

  • Strong engineering foundation.
  • Experience working in or with government-focused organizations.
  • Proficiency in Python and/or TypeScript.
  • Experience scoping and shipping features with product and engineering teams.

Responsibilities

  • Operate across strategic accounts, providing technical depth on complex engagements.
  • Own the technical direction and success of customer accounts and deployments.
  • Translate customer pain points into product direction: scope requirements, define solutions, and drive implementation.
  • Be the connective link between our Federal GTM team and core product engineering teams.
  • Build product extensions and integrations using Cognition’s APIs and platform primitives.
  • Architect and maintain complex enterprise deployments.
  • Handle escalated technical issues requiring deep debugging or architectural judgment.

Skills

Python
TypeScript
Cross-functional teamwork
Gov't experience
Feature shipping

Job description

Cognition, an applied AI lab building end-to-end software agents, seeks a foundational engineer for its newly formed federal group in Washington, DC. You will shape technical direction, own deployment success, and influence product strategy across federal accounts while driving growth of a fast-growing, mission-driven team.

You will work across strategic accounts, own critical architecture decisions, and translate field learnings into product requirements, ensuring scalable, enterprise-grade

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