обновлено 4 дня назад
Reinforcement Learning Engineer (AI)
96 000 - 120 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Reinforcement Learning Engineer (AI): Designing, training, and deploying reinforcement-learning systems for complex decision-making problems with an accent on reward modeling, simulation environments, and production-scale policy engineering. Focus on training neural network policies on GPU clusters, evaluating stability and safety, and improving RL systems after deployment.
Location: 100% remote within the United States
Salary: $96,000–$120,000 annually
Company
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 workflows.
- Train neural-network policies on GPU clusters and evaluate them at scale.
- Move RL solutions from research into production with a focus on stability, safety, and continuous improvement.
- Communicate research and engineering results through shipped systems or impactful publications.
Requirements
- Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience.
- At least 6 years of combined reinforcement-learning research and engineering experience; the posting also lists 8+ years of experience.
- Strong proficiency in Python and modern deep-learning frameworks.
- Hands-on experience with a major RL library or an in-house RL stack.
- Strong understanding of probability, optimization, and the theoretical foundations of reinforcement learning.
- U.S. work authorization is required; new H-1B visa petitions cannot be sponsored.
Nice to have
- Experience with RLHF for large language models.
- Experience with multi-agent or hierarchical reinforcement learning.
- Exposure to robotics, control systems, or autonomous driving.
- Publications or open-source contributions related to RL libraries or environments.
Culture & Benefits
- Full-time direct W-2 employment.
- Remote work within the United States.
- Career growth opportunities within an established technology organization.
- Equal employment opportunity across hiring and employment practices.
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