• 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

• Develop and implement statistical and machine learning models supporting readiness assessment, performance analysis, and injury-risk awareness. • Apply appropriate analytic methods based on data characteristics, scientific guidance, and Government priorities. • Ensure analytic approaches are transparent, interpretable, and suitable for operational decision support. • Support validation, testing, and refinement of analytic models under Government and senior scientific direction. • Assess model performance, limitations, assumptions, and potential bias. • Collaborate with epidemiologists and research psychologists to ensure analytic outputs align with scientific intent. • Work with data engineers to support feature development, data preparation, and analytic dataset construction. • Assist in identifying data quality issues that may impact model performance. • Ensure analytic inputs align with approved data definitions and governance standards. • Collaborate with software teams to support integration of analytic outputs into dashboards, reports, and user-facing decision-support tools. • Support development of repeatable analytic workflows and documentation. • Assist in troubleshooting analytic issues in coordination with data and software teams. • Contribute to documentation of analytic methods, assumptions, limitations, and results. • Support preparation of analytic summaries, briefings, and materials for Government stakeholders. • Clearly communicate analytic findings in a manner appropriate for non-technical audiences.