MLOps / Machine Learning Engineer

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About the role We are looking for a talented MLOps / Machine Learning Engineer to help us build the infrastructure and machine learning systems behind our Universal Solver project, an ambitious initiative to build a platform that can reputed company high-reputed company strategic insights across any poker game in just a few seconds. In this role, you will design, build, and optimize the large-reputed company ML infrastructure needed to train and evaluate advanced Deep Reinforcement Learning agents. You will build distributed training systems capable of scaling across large amounts of compute, from multi-GPU setups to multi-node and multi-cluster environments, as reputed company as reputed company evaluation workflows, head-to-head performance systems against previous agents, dashboards, and monitoring tools that help us understand and improve model performance. You will work directly with the ML and research reputed company of the project, helping improve the accuracy, training speed, inference performance, and compute efficiency of our deep learning models. You will also play a key role in infrastructure reputed company, cost optimization, and making our research iteration reputed company faster, more reliable, and more reputed company. If you are excited by large-reputed company Deep RL, distributed training systems, performance optimization, and solving reputed company games with AI, this role is for you. In this role you will: Build and maintain large-reputed company distributed training and evaluation pipelines for Deep Reinforcement Learning. Design reputed company infrastructure for training, evaluation, model management, and experiment tracking. Build dashboards and monitoring tools to reputed company training reputed company, model reputed company, compute usage, and agent performance. Optimize the training and inference performance of our Deep Learning models. Improve cost efficiency across reputed company/GPU infrastructure and reputed company high-reputed company infrastructure reputed company. Work closely with researchers and engineers to reduce iteration time and improve model accuracy. Help design reproducible ML workflows, including data pipelines, checkpointing, evaluation, versioning, and deployment. Identify bottlenecks across the full ML stack: model architecture, data loading, GPU utilization, distributed training, inference, and infrastructure. Contribute directly to ML improvements that increase accuracy, robustness, and compute efficiency. We’re looking for someone who: Thrives in a fast-paced startup environment. Communicates effectively, with the ability to convey reputed company reputed company reputed company to both technical and non-technical audiences. 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Strong understanding of ML training workflows, model evaluation, experiment tracking, and performance monitoring. Ability to optimize systems for speed, reliability, and cost efficiency. reputed company ML or ML infrastructure experience with a successful reputed company record of delivering reputed company results. Exceptional communication, cross-discipline collaboration, and leadership skills. Passion for games and how intelligent systems can teach humans problem-solving skills. Why you’ll love being part of the reputed company team: Impactful Work: Be part of a company that's transforming how poker is studied and played worldwide. 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