GTO Wizard

Research Scientist

• Conduct cutting-edge research and development in reinforcement learning and game theory
• Act as a technical expert for other researchers and engineers, guiding the research of multiple endeavors at the same time
• Collaborate with world-class researchers and engineers to bring research ideas to production
• Create prototypes targeting practical adoption in GTO Wizard
• Keep up-to-date with the latest research and development in the field by constantly reviewing scientific literature

Senior Research Scientist

Machine Learning Engineer

• Improve the performance of our machine learning models
• Continuously oversee the performance of models in production and develop monitoring tools to gain deeper insights into areas for model performance enhancement
• Collaborate with a team of researchers and engineers to design, implement and maintain innovative solutions to complex problems.
• Reduce the training and inference time of our ML pipeline
• Drive projects and lead new initiatives in MLOps

Machine Learning Scientist

• 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.

MLOps – Machine Learning Engineer

Product Designer

• Establish our company’s approach to product design.
• Foster professional growth of our other designers.
• Ensure our MUI-based design system is wisely utilized, maintained, and updated.
• Get hands-on to solve our most challenging design problems.
• Speak directly with at least one customer per week to keep in touch with user needs.
• Lead the evolution of design practices to incorporate AI, adjusting approach as tool capabilities broaden and quality improves.
• Contribute to an environment of rapid iteration: ship quickly and learn instead of analysis paralysis.

Lead Product Designer

Product Manager

• Facilitate rapid delivery and iteration of new product features and improvements.
• Use AI tooling as a daily part of your work.
• Talk with customer(s) directly every week.
• Achieve success through delivery rather than policy.
• ‘Make the call’, expressing our company preference for agency and action over excessive discussion, escalation, and delay.