• Experiment with novel language model architectures, helping drive and execute Fastino's research roadmap • Optimize Fastino’s multimodal models to improve response quality, instruction adherence, and overall performance metrics • Architect data processing pipelines, implementing filtering, balancing, and captioning systems to ensure training data quality across diverse content categories • Implement reinforcement learning techniques including Direct Preference Optimization and Generalized Reward Preference Optimization to align model outputs with human preferences and quality standards • Build robust and real-world motivated evaluations • Partner with Fastino engineering team to ship model updates directly to customers • Establish best practices for code health and documentation on the team, to facilitate collaboration and reliable development