Lead the development and evolution of our AI core, currently built in C++, towards the next generation of algorithms and architectures.
The main responsibility is to lead the evolution of our AI core to the next level.
Included the opportunity to get visibility as an industry expert through the publishing of papers, conferences, and so on, to make it more appealing.
Design, implement, and optimize advanced RL algorithms tailored for imperfect information games.
Research and integrate state-of-the-art techniques in RL, neural networks, and game theory to enhance AI performance and scalability.
Collaborate with cross-functional teams to identify challenges and innovate AI-driven solutions.
Evaluate and fine-tune AI models for decision-making, strategy optimization, and real- time applications in gaming.
Profile, optimize, and troubleshoot the AI core for high-performance execution across different computing architectures (e.g., CPU, GPU, or custom accelerators).
Document methodologies, experiments, and results to ensure transparency and Skills and Qualifications :
Bachelors, Masters, or Ph.D. in Computer Science, Mathematics, Physics, Artificial Intelligence, or a related field.
3+ years of experience in AI/ML, with a strong focus on Reinforcement Learning and Neural Networks.
Proficiency in programming languages commonly used in AI, such as C++, Python, Julia, or others relevant to your expertise.
In-depth understanding of game theory, especially concepts like Nash equilibrium and strategies in imperfect information games.
Expertise in RL frameworks and tools like OpenSpiel, RLlib, or similar libraries tailored for game AI.
Strong knowledge of RL algorithms, and experience with neural network architectures.
Familiarity with parallel computing, performance profiling, and optimization techniques.
Excellent problem-solving skills, with the ability to work independently and in a Skills :
Experience with multi-agent RL or hierarchical RL in gaming contexts.
Background in poker AI or similar imperfect information games.
Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Knowledge of distributed systems, parallel programs, or cloud-based AI deployments.
Published research or contributions to open-source AI projects in RL or game AI.
Soft Skills
Effective communication for collaborating with cross-functional teams and presenting complex ideas clearly.
Passion for innovation and driving the next generation of AI in gaming.
(ref:hirist.tech)
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