• Design, develop, train, and optimize ML models and AI solutions to solve complex business challenges. • Collaborate with Data Engineers on data collection, preprocessing, and feature engineering for model training and evaluation. • Evaluate, validate, and fine-tune models to ensure accuracy, scalability, and business impact. • Deploy, monitor, and maintain ML models in production, identifying opportunities for continuous improvement. • Work cross-functionally with engineers, data scientists, and business teams to deliver end-to-end ML solutions aligned with business needs.

• Design, train and improve upon cutting-edge models. • Help us develop new techniques to train and serve models safer, better, and faster. • Train extremely large-scale models on massive datasets. • Explore continual and active learning strategies for streaming data. • Learn from experienced senior machine learning technical staff. • Work closely with product teams to develop solutions.

• Design, train/post-train/fine-tune, and evaluate foundational algorithmic solutions with applications in a variety of domains in the VFX, animation, and games space, pushing forward the state of the art in CV as needed • Develop reusable foundational components and best practices, such as dataset curation, large-scale training, and post-training processes, which can be used by other scientists and engineers across teams • Work cross-functionally with engineers, scientists, artists, and product leaders to help identify and prioritize strategic research investment opportunities and problem requirements • Expand technical depth and domain expertise into new/adjacent areas as the business needs and the state of technology evolve

• Partner with Product, Marketing, Operations, and other teams to identify where ML can drive measurable value • Translate business problems into clear modeling objectives, metrics, and experimentation plans • Ensure ML efforts remain tightly aligned with business priorities and user impact • Lead the design, development, and iteration of ranking, filtering, and personalization models across Gametime’s product surfaces • Own modeling approaches, feature strategy, evaluation metrics, and offline and online experimentation • Balance relevance, revenue, and user trust when evolving ranking solutions • Apply LLMs and hybrid ML techniques to use cases such as semantic understanding, intent detection, content generation, and internal workflows • Evaluate emerging tools and techniques, recommending pragmatic adoption where they provide clear benefit • Establish best practices for testing, deploying, and monitoring LLM-powered models in production • Manage and mentor applied ML practitioners, supporting growth in technical depth and business impact • Set high standards for modeling rigor, experimentation discipline, and production readiness • Collaborate closely with ML engineering and platform teams to ensure scalable and reliable deployment