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Director, Applied Machine Learning
gametimeunited • United States - Remote
Posted: January 15, 2026
Job Description
Position Summary
Gametime is seeking a Director 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
Additional Content
Position Summary
Gametime is seeking a Director 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