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2 дня назад

Applied Researcher (AI)

150 000 - 300 000$
Формат работы
remote (только USA)
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
UK/US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
Applied Researcher (AI): Building applied reinforcement-learning programs, agent systems, evaluations, and deployment loops for customer workflows with an accent on post-training, experimental rigor, and practical impact. Focus on designing environments and reward signals, separating model limitations from infrastructure failures, and turning successful experiments into reusable research and platform capabilities.

Location: San Francisco, New York, London, or remote

Cash compensation: $150,000–$300,000 per year plus equity incentives

Company

hirify.global builds an open superintelligence stack for frontier AI labs, enterprises, and research teams, including compute, environments, evaluations, secure sandboxes, training, and deployment infrastructure.

What you will do

  • Own applied reinforcement-learning programs from ambiguous customer problems through deployed improvement loops.
  • Turn workflows and business goals into tasks, environments, evaluations, reward signals, research plans, and baselines.
  • Run RL, distillation, and other post-training experiments tied to deployment outcomes.
  • Build agent systems supporting tool use, memory, recovery, long horizons, and human oversight.
  • Deploy successful experiments, measure customer impact, and identify reusable environments, recipes, data loops, and platform capabilities.
  • Work directly with customer researchers, engineers, leaders, and Prime research, engineering, and product teams.

Requirements

  • Relevant experience in customer-embedded technical work, research engineering, technical founding, applied ML consulting with real build ownership, or end-to-end post-training programs.
  • Comfort working with senior external teams and connecting experimental evidence to business decisions without lowering the research bar.
  • Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
  • Strong experimental judgment, including meaningful baselines, careful trace inspection, failure attribution, and changing direction when evidence changes.
  • Strong Python skills and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
  • Comfort owning ambiguous problems and keeping partners and teammates current while resolving them.

Nice to have

  • Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work.

Culture & Benefits

  • Flexible work from San Francisco, New York, London, or remotely.
  • Visa sponsorship and relocation support.
  • Professional development budget.
  • Team off-sites and conference attendance.
  • Equity incentives in addition to cash compensation.

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