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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of Applied Machine Learning - **Company:** Gametime United - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $292,033.0 - $343,568.0 - **Contract:** Permanent contract - **Skills:** Artificial Neural Networks, Code Review, Machine Learning, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Information Technology, Xgboost - **Published:** August 6, 2026 - **Apply:** https://www.sanfranciscogigs.com/job.asp?id=3347382792&tx=HT7266TYT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 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, * 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 ## Description 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., * 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 ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Are Code Reviews Worth It? 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