Job Description:
Reinforcement Learning Engineer – Train Smart Agents
The Reinforcement Learning Engineer specializes in designing and implementing advanced algorithms where AI agents learn optimal behaviors by interacting with their environment through trial and error. Your primary goal is to create models that maximize long-term rewards, enabling intelligent decision-making in complex digital media applications.
In this role, you will focus on building reinforcement learning (RL) solutions that power personalization engines, recommendation systems, dynamic content optimization, and automation tools. These applications rely on adaptive models that continuously improve based on user interactions and evolving data patterns.
Collaboration is a critical aspect of your work. You’ll partner closely with data scientists, software engineers, and product teams to develop scalable and efficient RL algorithms. Together, you will integrate these models seamlessly into client platforms, ensuring they deliver measurable value and enhance user experiences.
Your responsibilities include researching new RL methods, experimenting with model architectures, tuning hyperparameters, and validating performance through rigorous testing. You’ll also troubleshoot challenges such as sparse rewards, exploration-exploitation trade-offs, and environment complexity to improve model robustness.
Beyond development, you will contribute to documentation, share best practices, and help build reusable RL frameworks to accelerate innovation within the consultancy. Staying current with the latest academic research and industry trends in reinforcement learning is essential to maintaining state-of-the-art solutions.
The role demands strong programming skills, a solid foundation in machine learning and AI theory, and the ability to translate complex algorithms into practical, production-ready systems. As a Reinforcement Learning Engineer, you play a key role in driving the next generation of AI-powered digital media tools that adapt, learn, and evolve to meet client needs.
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Responsibilities:
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Design and implement reinforcement learning algorithms.
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Build simulation environments for training RL agents.
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Optimize policies for improved decision-making.
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Collaborate with cross-functional teams to integrate RL solutions.
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Monitor model performance and conduct iterative improvements.
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Stay updated on RL research and technologies.
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Write clean, maintainable code and documentation.
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Troubleshoot and debug RL systems.
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Assist in deploying RL models in production.
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Mentor junior team members and share knowledge.
Preferred Qualifications:
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3+ years experience in reinforcement learning or related AI fields.
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Proficiency in Python and RL frameworks (e.g., OpenAI Gym, RLlib).
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Strong background in machine learning and statistics.
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Experience with deep learning frameworks (TensorFlow, PyTorch).
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Understanding of Markov Decision Processes and dynamic programming.
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Bachelor’s or Master’s degree in computer science, AI, or related discipline.
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Excellent problem-solving and analytical skills.
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Ability to work collaboratively in agile teams.
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Familiarity with cloud computing and deployment.
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Passion for advancing AI through RL.
