Research Scientist

<p>A scientist who codes – advancing RL with new, generalizable ideas.</p><p></p><p>### About the Role</p><p>As a Research Scientist at Void Main Lab, you will explore and develop <strong>reinforcement learning (RL) methods, synthetic data generation, and agent architectures</strong> that enable AI systems to not only imitate but invent. You will collaborate with a cross-functional team of researchers and engineers to push the boundaries of AI-native creation in video, gaming, and software. </p><p></p><p>### Responsibilities</p><p>- Conduct original research in reinforcement learning, reward design, and agent-based systems. </p><p>- Design and implement algorithms that generalize across domains and tasks. </p><p>- Explore new paradigms for synthetic data generation and long-context learning. </p><p>- Work closely with product and engineering teams to translate research into practical applications. </p><p>- Publish high-quality research in top conferences and journals, and contribute to the global research community. </p><p>- Drive innovation by staying ahead of emerging trends in AI, ML, and computational creativity. </p><p></p><p>### Requirements</p><p>- <strong>PhD in Computer Science, Machine Learning, Mathematics, or related field</strong>, or a <strong>Master’s degree with outstanding research/industry experience</strong> . </p><p>- Strong background in <strong>reinforcement learning, optimization, or probabilistic modeling</strong> . </p><p>- Proficiency in <strong>Python</strong> and at least one major ML framework (e.g., PyTorch, JAX, TensorFlow).</p><p>- Strong publication record in top AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR).</p><p>- Ability to design, implement, and evaluate new algorithms from scratch. </p><p>- Excellent problem-solving skills, creativity, and a drive for pushing the boundaries of AI. </p><p>- Strong communication and collaboration skills; ability to work in a fast-paced, research-driven environment. </p><p></p><p><strong>Preferred:</strong> </p><p>- Experience with large-scale distributed training and experimentation. </p><p>- Familiarity with generative models, multimodal learning, or self-play environments. </p><p>- Passion for exploring the frontier of AI creativity and building AI-native systems. </p>