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8 часов назад

Applied AI Engineer (AI)

190 000 - 240 000$
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Applied AI Engineer (LLM/Agentic Systems): Design and build production AI systems for matchmaking, ranking, and automation across a recruiting marketplace with an accent on LLM applications, retrieval pipelines, agentic workflows, and measurable product impact. Focus on building evaluation frameworks, balancing model quality, cost, latency, and reliability, and shipping AI features end-to-end with product and engineering teams.

Location: San Francisco, United States

Salary: $190,000–$240,000 per year plus equity

Company

hirify.global is an AI recruiting marketplace connecting companies with specialized recruiters and AI agents to make hiring faster, more reliable, and scalable.

What you will do

  • Design and build AI systems powering matchmaking, ranking, and automation across the marketplace.
  • Develop LLM-driven features, including retrieval pipelines, agentic workflows, and AI-powered automation.
  • Own AI systems end to end, from data pipelines and prompt or model design through deployment, monitoring, and production iteration.
  • Work with product, ML engineering, and full-stack teams to ship capabilities that improve marketplace metrics.
  • Design evaluation frameworks for real-world performance, reliability, and business outcomes.
  • Define production AI best practices while balancing quality, cost, latency, and maintainability.

Requirements

  • 2–5 years of experience building and shipping AI systems used by real people.
  • Experience at a growing AI-native startup from Series A to Series D and with products serving many users.
  • Strong Python and TypeScript experience.
  • Experience building agentic systems that produce real business or user outcomes.
  • Strong product judgment and the ability to explain technical tradeoffs to non-technical partners.
  • Ability to work effectively in ambiguous zero-to-one environments.

Nice to have

  • Experience building LLM-powered applications, retrieval systems, tool-using agents, or AI-driven automation.
  • Familiarity with traditional ML techniques such as ranking, recommendation, or classification.

Culture & Benefits

  • Equity offered as part of the compensation package.
  • Work on infrastructure combining human recruiting expertise with modern AI tools.
  • Build products for a large and fragmented hiring market.
  • Collaborate across product, ML engineering, and full-stack functions.

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