MLOps / Machine Learning EngineerVerified as a real position

Who we’re looking for

• 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.

What you’ll do

• 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.

About the position

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.

Benefits

• 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.