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.
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
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
- 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
- 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
At Gametime pay ranges are subject to change and assigned to a job based on specific market median of similar jobs according to 3rd party salary benchmark surveys. Individual pay within that range can vary for several reasons including skills/capabilities, experience, and available budget.
Gametime is committed to bringing together individuals from different backgrounds and perspectives. We strive to create an inclusive environment where everyone can thrive, feel a sense of belonging, and do great work together. As an equal opportunity employer, we prohibit any unlawful discrimination against a job applicant on the basis of their race, color, religion, veteran status, sex, parental status, gender identity or expression, transgender status, sexual orientation, national origin, age, disability or genetic information. We respect the laws enforced by the EEOC and are dedicated to going above and beyond in fostering diversity across our company.
Skills Required
- Bachelor's degree in Computer Science, Engineering, or a related field
- 6+ years of experience building and deploying production machine learning models
- Experience owning ranking, recommendation, or personalization systems
- Strong foundation in learning-to-rank, embeddings, gradient boosting, and neural networks
- Hands-on experience with LLMs, including prompt engineering, fine-tuning, retrieval-augmented generation, and evaluation
- Solid software engineering skills and experience with modern data and machine learning stacks
- Ability to work cross-functionally and influence without relying on hierarchy
- Advanced degree
Gametime Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Gametime and has not been reviewed or approved by Gametime.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is highlighted, with self‑funded plans and HSA/FSA options noted alongside wellness initiatives. Some indications of company‑paid premiums further point to strong cost coverage.
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Equity Value & Accessibility — Equity is presented as a standard component across many roles, and stock options are called out positively. Technical and senior opportunities commonly feature equity alongside competitive cash.
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Wellbeing & Lifestyle Benefits — Monthly credits to attend live events, a remote‑first setup with home‑office support, and periodic meetups are emphasized. These lifestyle‑oriented perks add meaningful value beyond base pay.
Gametime Insights
What We Do
Gametime sells last-minute tickets to the most popular events in sports, music, and theater in more than 50 cities across the U.S. and Canada. We’re passionate about enabling incredible shared experiences, so we build technology that gets people out into the real world together. With a bias for spontaneity, we’re focused on making the process simple and smooth while you’re on the move. We eliminated the need for printing tickets and built a better way to access the best live experiences right from your phone – so you can skip the hassle and enjoy the moment.
