MLOps / Machine Learning Engineer

GTO Wizard is the leading poker training tool, trusted by top players and industry brands worldwide. Recognized as the #1 educational resource in poker, we’re revolutionizing poker education and providing thousands of players with the tools to elevate their game. Our global team thrives on a culture of autonomy, responsibility, and excellence, empowering talented professionals to grow and succeed as part of a fast-growing company. If you're passionate about poker, eager to shape the future of the game, and ready to move up in stakes by joining a company that values passion, growth, and innovation, join us in redefining how poker is studied and played.

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 provide high-quality strategic insights across any poker game in just a few seconds.  

In this role, you will design, build, and optimize the large-scale 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 well as complex 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 side 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 decisions, cost optimization, and making our research iteration loop faster, more reliable, and more scalable.

If you are excited by large-scale Deep RL, distributed training systems, performance optimization, and solving complex games with AI, this role is for you.

• Build and maintain large-scale distributed training and evaluation pipelines for Deep Reinforcement Learning.

• Design scalable infrastructure for training, evaluation, model management, and experiment tracking.

• Build dashboards and monitoring tools to track training progress, model quality, compute usage, and agent performance.

• Optimize the training and inference performance of our Deep Learning models.

• Improve cost efficiency across cloud/GPU infrastructure and make high-impact infrastructure decisions.

• 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.

• Thrives in a fast-paced startup environment.

• Communicates effectively, with the ability to convey complex ideas clearly to both technical and non-technical audiences.

• Has sharp analytical skills to approach complex problems methodically, think creatively, and develop innovative solutions in an evolving field.

• Enjoys working at the intersection of ML research, infrastructure, and engineering.

• Takes ownership of ambiguous problems and can turn research needs into reliable, scalable systems.

• Cares deeply about correctness, reproducibility, performance, and cost efficiency.

• Is enthusiastic about mentoring and collaborating with colleagues, providing constructive feedback, and helping the team deliver high-quality, impactful outcomes.

• Strong software engineering skills and experience building reliable production-quality systems.

• Hands-on experience with PyTorch or similar deep learning frameworks.

• Experience building infrastructure for machine learning training and evaluation.

• Experience with distributed training at scale across GPUs or clusters.

• Strong understanding of ML training workflows, model evaluation, experiment tracking, and performance monitoring.

• Ability to optimize systems for speed, reliability, and cost efficiency.

• Applied ML or ML infrastructure experience with a successful track record of delivering quality results.

• Exceptional communication, cross-discipline collaboration, and leadership skills.

• Passion for games and how intelligent systems can teach humans problem-solving skills.

• Impactful Work: Be part of a company that's transforming how poker is studied and played worldwide.

• Innovative Environment: Work with cutting-edge technology and contribute to a platform that's pushing the boundaries of poker strategy.

• Professional Growth: We support your personal and professional development with opportunities to learn new skills and take on exciting challenges.

• Collaborative Culture: Join a team where your ideas are valued, and you can make a real impact in a supportive, inclusive environment.

• Flexible Work Arrangements: Enjoy the benefits of remote work while collaborating with a global team.

• Passionate Community: Engage with a vibrant community of poker enthusiasts and professionals who are passionate about the game.