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RL Engineer (AI)

80 000 - 100 000$
Формат работы
remote (только USA)
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
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
RL Engineer (AI): Designing, training, and deploying reinforcement-learning systems for high-impact decision-making with an accent on reward modeling, simulation environments, and policy training at scale. Focus on training neural-network policies on GPU clusters, evaluating production systems for stability and safety, and translating RL research into deployable solutions.

Location: 100% remote within the United States

Salary: $80,000–$100,000 annually

Company

hirify.global is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

  • Design, train, and deploy reinforcement-learning systems for complex decision-making problems.
  • Develop and tune reward functions for non-trivial environments.
  • Build and use simulation environments and large-scale experience-collection pipelines.
  • Train and evaluate neural-network policies on GPU clusters.
  • Move RL solutions from research into production with a focus on stability, safety, and continuous improvement.
  • Communicate technical findings and contribute to impactful RL work through shipped systems or publications.

Requirements

  • Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience.
  • Six or more years of combined reinforcement-learning research and engineering experience.
  • Strong proficiency in Python and modern deep-learning frameworks.
  • Hands-on experience with a major RL library or an in-house RL stack.
  • Understanding of probability, optimization, and the theoretical foundations of reinforcement learning.
  • Eligible to work in the United States; new H-1B visa petitions cannot be sponsored.

Nice to have

  • Experience with RLHF for large language models.
  • Familiarity with multi-agent or hierarchical reinforcement learning.
  • Exposure to robotics, control systems, or autonomous driving.
  • Publications in RL or related research venues.
  • Open-source contributions to RL libraries or environments.

Culture & Benefits

  • Full-time direct W2 employment.
  • Remote work within the United States.
  • Career growth opportunities within an established technology organization.
  • Equal employment opportunity and a workplace free from harassment and discrimination.

Hiring process

  • Submit a resume for consideration.

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