Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Зарплата и рынок
Мало
$150к-300к/год
$220к$260кмедиана$330к
По похожим ролям (49) ~ $260к/год
На СНГ рынке: $4.2к/мес ($3.6к - $5.3к)
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Сильные задачи на стыке frontier AI и исследований, понятная зона ответственности и крупное финансирование делают вакансию привлекательной, но зарплата ниже ориентира для похожих ролей.
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Ниже рынкаШирокая ответственностьСовременный стекПередовой ИИСильные инвестиции
Оценка от Hirify AI
Мэтч & Сопровод
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Описание вакансии
Текст:
Hirify AI/Оригинал
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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