Note: The job is a remote job and is open to candidates in USA. Gametime is a company focused on uniting the world through shared experiences, particularly in the event ticket industry. They are seeking a Head of Applied Machine Learning to lead the development and application of machine learning models that drive business impact across various functions. The role involves working closely with multiple teams to identify opportunities, translating them into modeling problems, and delivering production-ready solutions.

Responsibilities

• 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

Skills

• Bachelor's degree in Computer Science, Engineering, or a related field
• 6+ years of experience building and deploying production machine learning models
• Demonstrated experience owning ranking, recommendation, or personalization systems
• Strong foundation in applied ML techniques such as learning-to-rank, embeddings, gradient boosting, and neural networks
• Hands-on experience working with LLMs, including prompt engineering, fine-tuning, retrieval-augmented generation, and evaluation
• Solid software engineering skills and experience working within modern data and ML stacks
• Proven ability to work cross-functionally and influence without relying on hierarchy
• Advanced degree preferred

Company Overview

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