Head of Applied Machine Learning

GameTime; United States; À distance; USD 292 000 - 344 000; Plein temps

Résumé du poste

Gametime is seeking a Head of Applied Machine Learning to lead production ML models, ranking, and personalization across the platform. This hands-on leader will collaborate with Product, Data, and Engineering to translate business problems into robust ML solutions that move key metrics.

The role requires 6+ years of ML experience, strong ranking and LLM expertise, and a track record of mentoring teams while delivering business impact in a fast-paced environment.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field
• 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

Responsabilités

• Identify where ML can drive measurable value across product, marketing, operations, and other teams
• 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 surfaces
• Own modeling approaches, feature strategy, evaluation metrics, and offline/online experimentation
• Balance relevance, revenue, and user trust in ranking solutions
• Apply LLMs and hybrid ML techniques to use cases like semantic understanding and content generation
• Establish best practices for testing, deploying, and monitoring LLM-powered models in production
• Manage and mentor applied ML practitioners and collaborate with ML engineering teams

Connaissances

Ranking systems

Personalization

ML modeling

LLMs

Prompt engineering

Cross-functional leadership

Software engineering

Production ML

Formation

Bachelor's degree in Computer Science, Engineering, or related field

Outils

LLMs

Retrieval-augmented generation

Description du poste

About Us:

Live experiences help people cross today’s digital divide and focus on what truly connects us – the here, the now, this once-in-a-lifetime moment that’s bringing us together. To fulfill Gametime’s mission of uniting the world through shared experiences, we make it easy for people to discover and access the live experiences that matter most.With platforms on iOS, Android, mobile web and desktop supporting more than60,000events across the US and Canada, we are reimagining the event ticket industry in order to move at the speed of life.

Position Summary

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

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

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.

United States - Pay Range$292,033—$343,568 USDGametime 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.