Machine Learning Engineer, Gametime

• Proven experience in the end-to-end development and deployment of machine learning solutions, including successfully transitioning models to production environments
• Strong programming proficiency, particularly in Python and SQL, coupled with a solid understanding of software engineering best practices and principles as applied to machine learning system development
• Experience with cloud platforms such as AWS, Azure, or GCP, and familiarity with MLOps practices
• Hands-on experience with key machine learning frameworks and libraries (e.g., PyTorch, Scikit-Learn, XGBoost) and expertise in relevant AI subfields such as Deep Learning, Natural Language Processing (NLP), and Computer Vision
• Familiarity with Large Language Models (LLMs) and other advanced AI tools, including potential applications of AI agents, and a strong interest in staying updated on the latest developments in the field
• Ability to collaborate effectively across teams and communicate with key stakeholders
• A proactive self-starter with a strong ability to learn quickly and work independently, demonstrating the desire and readiness to manage an increasing level of responsibility
• Experience: 5+ years of industry experience

Desirable

• The Machine Learning team is responsible for designing, developing, and deploying cutting-edge AI/ML solutions, owning the full lifecycle from ideation to production
• In this role, you will work closely with the ML Platform and Data Science teams to maximize the impact of ML across every area of the business
• This is an opportunity to join a growing team and help shape its future direction
• Designing & Implementing AI/ML Solutions: Lead the design and implementation of robust, scalable, and high-performance machine learning applications that power critical user experiences and business operations
• End-to-End Ownership: Take ownership of the full machine learning lifecycle for various applications, working closely with data scientists, product managers, and backend engineers to define problems, develop solutions, and measure impact
• Driving Business Impact: Identify opportunities to apply machine learning to solve complex business challenges, directly contributing to key metrics such as user engagement, conversion, and overall revenue
• Leveraging & Contributing to ML Platform: Partner with the Machine Learning Platform team to utilize existing infrastructure and provide critical feedback and requirements to shape the platform's future direction
• Setting Technical Standards: Champion best practices for MLOps, code quality, testing, and deployment, ensuring the reliability and maintainability of our ML application stack
• Evangelize ML throughout the organization: Communicate results and methodology effectively within the team and to external stakeholders