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3 дня назад

Applied Research Role (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

Текст:
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TL;DR
Applied Research Role (AI): Building open-source reinforcement learning, environment, evaluation, and agent systems for a full-stack frontier AI training platform with an accent on reproducible research, researcher-facing interfaces, and experimental rigor. Focus on turning research workflows into stable APIs, inspectable training artifacts, and contributor-ready systems while debugging model-training and post-training loops.

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

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

Company

hirify.global is building an open superintelligence stack for frontier AI research, including compute, environments, evaluations, secure sandboxes, training, and deployment infrastructure.

What you will do

  • Own important open-source reinforcement learning, environment, evaluation, and agent systems.
  • Build researcher-facing features for the training platform in collaboration with engineering and design.
  • Develop APIs, examples, reference implementations, and technical explanations for useful research abstractions.
  • Work with research users and external contributors to improve interfaces, inspectable code, and research artifacts.
  • Help researchers move from environment ideas to credible evaluation and training runs.
  • Turn internal agent and reinforcement learning abstractions into stable public interfaces and create contributor paths for environments, harnesses, algorithms, and integrations.

Requirements

  • Experience owning open-source machine learning or developer-tool systems, research engineering, technical product work, or communities built around code and reproducible research.
  • Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely related experimental loops.
  • Strong experimental judgment, including meaningful baselines, careful trace inspection, failure attribution, and adapting 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 informed while resolving them.

Nice to have

  • Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work.
  • Examples of difficult work built, failures encountered, and how evidence changed the approach.

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.
  • Work directly with customers, users, research partners, and external contributors while maintaining a substantive research agenda.

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