O pozici
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.
Co budeš dělat
- 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.
Koho hledáme
- 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.
Benefity
- 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.
