Director of Applied Machine Learning

Role Overview

Lead the development and application of machine learning and LLM-powered models that deliver measurable business impact across Gametime’s functions. You’ll partner with Product, Data, Engineering, and Business teams to find high-value opportunities, define modeling problems, and ship production-ready ranking, filtering, and personalization systems.

Key Missions

• Partner with Product, Marketing, Operations, and others to identify ML opportunities that drive measurable value.
• Design, develop, and iterate ranking, filtering, and personalization models across Gametime product surfaces.
• Manage and mentor applied ML practitioners, growing both their technical depth and business impact.

Responsibilities

• Translate business opportunities into well-defined ML modeling tasks.
• Own ranking/recommendation/curation problems end-to-end, from framing through production iteration.
• Deliver production systems using a modern data/ML stack, with strong technical judgment.
• Review code and guide modeling decisions (hands-on leadership).
• Influence stakeholders cross-functionally without relying on hierarchy.

Requirements

• Demonstrated experience owning ranking, recommendation, or personalization systems.
• Strong technical judgment applying classical ML and LLM-based approaches.
• Hands-on LLM experience, including:
  • Prompt engineering
  • Fine-tuning
  • Retrieval-Augmented Generation (RAG)
  • Evaluation
• Strong applied ML foundation (e.g., learning-to-rank, embeddings, gradient boosting, neural networks).
• 6+ years building and deploying production machine learning models.
• Bachelor’s degree in CS/Engineering or related field (advanced degree preferred).
• Solid software engineering skills and experience in modern data/ML stacks.
• Ability to communicate complex ML concepts as business-relevant insights.

Nice-to-Haves / Desired Traits

• Business-oriented approach focused on context, tradeoffs, and outcomes.
• Curiosity and motivation to stay current with applied ML and LLM advancements.
• Player-coach mindset: mentoring plus active technical involvement.