aipythonstartupreinforcement learningagentsevaluationpost trainingresearch engineeringmachine learning systems
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
$200к$230кмедиана$270к
По похожим ролям (50) ~ $230к/год
На СНГ рынке: $4.4к/мес ($3.6к - $5.9к)
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Фронтирные AI-исследования, заметная руководящая роль и сильное финансирование делают вакансию перспективной, но указанная зарплата ниже медианы рынка, а работа только из офиса в Нью-Йорке ограничивает гибкость.
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Ниже рынкаТолько офисФронтирный AIРоль с влияниемСильное финансированиеСовременный стек
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Senior Regional Player-Coach (AI): Building and leading applied research programs for frontier AI post-training, agents, evaluations, and deployed systems with an accent on external partner programs, experimental rigor, and recruiting a New York research group. Focus on designing environments and reward signals, debugging training and evaluation loops, attributing failures, and turning validated results into shared research or product capabilities.
Location: Full-time, in-person work from the New York City office; relocation support and visa sponsorship are available.
Salary: $150,000–$300,000 cash compensation plus equity incentives.
Company
hirify.global is building an open superintelligence stack combining compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for frontier AI post-training.
What you will do
Establish the New York Applied Research group through personally led research and external field programs.
Shape customer and partner problems into research programs with clear owners, comparable evidence, and paths to shared capabilities.
Lead high-stakes programs through environment design, baselines, post-training, evaluation, failure analysis, and deployment.
Recruit and develop the founding New York researchers while setting technical and experimental standards.
Provide cross-program technical direction and decide which results become shared research, open-source systems, or product capabilities.
Requirements
Track record leading high-performing applied-research or research-engineering teams.
Deep credibility in reinforcement learning, post-training, agents, evaluation, or machine learning systems.
Exceptional recruiting judgment and experience with demanding external technical stakeholders.
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, clear failure attribution, and willingness to change direction when evidence changes.
Strong Python skills and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
Ability to own ambiguous problems and keep partners and teammates current while resolving them.
Willingness to remain directly involved in research, external programs, recruiting, and group development.
Nice to have
Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work.
Culture & Benefits
In-person work from the New York City office.
Professional development budget.
Team off-sites and conference attendance.
Equity incentives.
Visa sponsorship and relocation support.
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