Director, Applied Machine Learning
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Job Description
Director leading the development and application of machine learning models that drive business impact at Gametime. Overseeing ranking, curation, and personalization solutions to enhance customer experiences.
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
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.
Benefits:
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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