Head of Applied Machine Learning

Position Summary

Gametime is seeking a Head of Applied Machine Learning to lead the development and application of machine learning and LLM-powered models that drive meaningful business impact across product, marketing, operations, and other key functions. This role is ideal for a hands-on, applied ML leader who thrives at the intersection of modeling excellence and business understanding. You will work closely with Product, Data, Engineering, and business partners to identify high-value opportunities, translate them into well-defined modeling problems, and deliver production-ready solutions. A core focus of this role will be curation, including ranking, filtering, and personalization systems that directly shape the customer experience, alongside thoughtful application of modern LLM-based techniques.

Who You Are

• An experienced applied ML practitioner with a track record of delivering production models that move business metrics
• Deeply comfortable owning ranking, recommendation, and curation problems from framing through iteration in production
• Experienced applying both classical ML techniques and LLM-based approaches with strong technical judgment
• A player-coach who can review code, guide modeling decisions, and mentor ML practitioners
• Business-oriented, seeking context, tradeoffs, and outcomes rather than purely technical elegance
• Comfortable managing multiple initiatives across stakeholders and timelines
• A clear communicator who can translate complex ML concepts into business-relevant insights
• Curious and motivated to stay current with applied ML and LLM advancements

What You Will Work On

Applied ML and Business Alignment

• 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

Ranking, Curation, and Personalization

• 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

LLM and Advanced Modeling Applications

• 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

Team Leadership and Craft Excellence

• 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

Experience You Bring

• Bachelor’s degree in Computer Science, Engineering, or a related field (advanced degree preferred)
• 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

What Success Looks Like

• Applied ML solutions that measurably improve customer experience and business outcomes
• High-quality, continuously improving ranking and curation systems
• Thoughtful, value-driven use of LLMs rather than novelty applications
• Strong partnership with product and business teams, with ML viewed as a strategic enabler
• A supported, high-performing applied ML team delivering consistent impact