Director - Machine Learning Engineering

Key Skills

Effective communication skills, Machine learning techniques, Snowflake

Job Description

The Role:

We are looking for an organized, data-driven, and curious team player to work cross-functionally across the data, product, marketing, finance, and operations teams. As a Director of Machine Learning Engineering (MLE), you will lead the MLE function to deliver foundational technology to the future success of the company. We have a multitude of dynamic opportunities that sit at the core of our product strategy that are ripe for machine learning to enable us to optimize and excel. A successful leader in this role will have experience developing a MLE organization and establishing a technology platform that will enable Gametime to succeed for years to come. This role will be expected to lead ML projects across all domains of the company, grow and retain talent, and be a champion for best practices. This role is for a player-coach. We are looking for someone who still desires to be hands on in the codebase and working alongside the team to execute.

Who you are:

• A technical leader with proven experience creating production end-to-end machine learning infrastructure at scale.
• You are comfortable discussing modern ML architectures, personalization algorithms, dynamic pricing strategies, and solutions to ranking problems.
• You are able to dive deep into code to provide an expert opinion on design design choices and implementation
• Proven experience managing multiple initiatives, with many stakeholders, and several timelines simultaneously.
• Able to adapt and succeed in a rapidly changing landscape while maintaining a long term vantage
• A capable communicator who can change their altitude of discussion to move from discussing business impact and priorities with senior leadership and deep technical conversations with the development team
• A life long learner who is passionate about staying at the forefront of industry trends and developments to ensure Gametime is performing at the highest capability level possible

What you will work on:

• Develop a Machine Learning organization and infrastructure that will support Gametime’s growth and success
• Establish a vision and goals for ML at Gametime that reflects the company’s desire to grow and succeed
• Hire, mentor, and grow the ML team members and ML skill set across the company
• Set high standards for engineering excellence and drive innovation
• Utilize industry best practices to consistently deliver high caliber, scalable solutions while managing competing priorities and cost
• Leverage your prior experience to develop new and innovative solutions that will reflect and incorporate rapidly changing industry trends
• Embody Gametime’s core values and enact them in you day-to-day leadership

Experience you bring:

• Bachelor's Degree in computer science, software engineering, or related field (Masters or Ph.D. preferred)
• 6 + years of experience in developing production machine learning capabilities
• Deep knowledge of software architecture and engineering best practices, especially modern cloud computing stacks for deploying machine learning and microservices at scale especially on Snowflake
• Strong background in machine learning capabilities. For example, this could include product recommendation engines, ranking problems, or dynamic pricing systems, etc
• Experience influencing platform development for providing foundational machine learning components for data scientists to deliver into production

What we offer

• Flexible PTO
• Equity
• Medical, dental, & vision insurance
• Life insurance and disability benefits
• 401K, HSA, pre-tax savings programs
• New equipment setup provided
• Diverse Family-forming benefits through Carrot Fertility
• Wellness programs
• Tenure recognition

$326,000 - $370,000 a year

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.