Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Роль сочетает прикладные исследования LLM с ответственностью за production-системы в финансируемом AI-стартапе: технический масштаб впечатляет, но сохраняются неопределённость и темп ранней компании.
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Пять дней в офисеПередовой AIПонятная зона ответственностиПотенциал стартапа
Оценка от Hirify AI
Мэтч & Сопровод
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
AI Research Engineer (LLM): Building production LLM systems and agentic workflows that learn enterprise processes from knowledge bases, support conversations, tickets, logs, and agent traces with an accent on context learning, agent orchestration, and evaluation. Focus on designing continuous-learning loops, confidence scoring, scalable AI orchestration, and mechanisms that determine when to automate or escalate to a human.
Location: London, United Kingdom; on-site five days a week from the office
Company
hirify.global is a Series A startup building AI agents that learn how enterprise processes operate and execute those processes. The company is backed by Sequoia and other leading venture capital firms, with offices in New York and London.
What you will do
Build and extend the core context-learning library, turning customer-informed problems into reusable platform capabilities.
Design and implement LLM-powered systems and agentic workflows from concept through production.
Develop agents that edit, update, and maintain large knowledge bases autonomously.
Build continuous-learning loops using human corrections and production outcomes to identify knowledge gaps, detect changed procedures, and improve prompts and instructions.
Develop evaluation frameworks, confidence scoring, and escalation logic for deciding when to automate or involve a human.
Architect asynchronous, scalable systems for complex AI orchestration and contribute to AI strategy and product capabilities.
Requirements
Experience building a complex production LLM system, including explaining engineering decisions and changes made after real users adopted it.
Practical depth in one or more of context engineering, retrieval, agent orchestration, memory, evaluation, or learning from feedback.
Ability to turn ambiguous problems into testable hypotheses, establish baselines, and distinguish genuine improvements from noise.
Interest in open-ended problems where solutions may not yet exist.
Comfort working across research papers and production code.
Strong communication skills for explaining work to teammates and customers.
3+ years of professional experience.
Culture & Benefits
In-person collaboration five days a week from the London or New York office.
Competitive base salary and meaningful equity.
Private medical and dental coverage in the UK; top-tier health, dental, and vision plans with 100% premium coverage in the US.
Flexible paid time off.
Lunch provided in the office most days, with snacks and drinks.
Workspace setup stipend.
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