Director, Applied Machine Learning

Gametime United

2d•$292,033 - $343,568

About The Position

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.

Requirements

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

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