MLOps Engineer

About the role

2K is headquartered in Novato, California and is a wholly owned label of Take-Two Interactive Software, Inc. (NASDAQ: TTWO). Founded in 2005, 2K Games is a global video game company, publishing titles developed by some of the most influential game development studios in the world. Our studios responsible for developing 2K's portfolio of world-class games across multiple platforms, include Visual Concepts, Firaxis, Hangar 13, CatDaddy, Cloud Chamber, 31st Union, HB Studios, and 2K SportsLab. Our portfolio of titles is expanding due to our global strategic plan, building and acquiring exciting studios whose content continues to inspire all of us! 2K publishes titles in today's most popular gaming genres, including sports, shooters, action, role-playing, strategy, casual, and family entertainment. Our team of engineers, marketers, artists, writers, data scientists, producers, thinkers and doers, are the professional publishing stewards of 2K's portfolio currently includes several AAA, sports and entertainment brands, including global powerhouse NBA®️ 2K, renowned BioShock®️, Borderlands®️, Mafia, Sid Meier's Civilization®️ and XCOM®️ brands; popular WWE®️ 2K and WWE®️ SuperCard franchises, TopSpin 2K25, as well as the critically and commercially acclaimed PGA TOUR®️ 2K At 2K, we pride ourselves on creating an inclusive work environment, which means encouraging our teams to Come as You Are and do your best work! We encourage ALL applicants to explore our global positions, even if they don't meet every requirement for the role. If you're interested in the job and think you have what it takes to work at 2K, we encourage you to apply! What We Need The MLOps Engineer is responsible for designing, implementing, and managing the end-to-end lifecycle of 2K's machine learning models. Beyond building individual models, this role focuses on creating the automated systems that facilitate training, deployment, monitoring, and retraining at scale. As an advanced professional, the MLOps Engineer ensures that ML infrastructure is robust, performant, and capable of adapting to evolving player behavior, providing the heavy lifting required for seamless ML deployment across 2K's global titles.

Responsibilities

• ML Lifecycle Automation: Lead the design and maintenance of end-to-end ML pipelines covering data ingestion, feature engineering, model training, and deployment.
• Model Serving Architecture: Architect and manage scalable model serving infrastructure (API-based or streaming) utilizing tools such as Databricks Model Serving, Seldon, or SageMaker.
• Feature Store Management: Implement and maintain a centralized feature store to ensure consistency and low-latency access between training and real-time inference.
• CI/CD for Machine Learning (CT): Develop and optimize Continuous Training (CT) pipelines that automate model retraining based on performance decay or new data availability.
• Monitoring & Observability: Implement specialized monitoring for ML assets, tracking model drift, feature skew, and prediction latency to ensure high-quality player experiences.
• Cross-Functional Collaboration: Partner with Data Scientists to refactor experimental code into production-ready, modular, and testable components.
• Resource & Cost Optimization: Manage and optimize the infrastructure costs of GPU/CPU clusters, ensuring training jobs are balanced for performance and budget efficiency.
• Who We Think Will Be A Great Fit
• Technical Leadership: Acts as a subject matter expert in ML infrastructure, guiding the technical direction of model deployment strategies.
• Operational Excellence: Focuses on building "BioShock-level" immersion through highly reliable and performant automated ML systems.
• Problem Solving: Diagnoses and resolves complex issues within non-deterministic ML code and distributed infrastructure.
• Influence & Collaboration: Mentors Data Scientists on software engineering best practices while effectively communicating infrastructure constraints to stakeholders.
• Agility: Quickly adapts ML strategies to support massive live-service ecosystems and changing data landscapes.
• Required Qualifications, Knowledge, and Job-Related Skills
• Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• Experience: 6+ years of professional experience in MLOps, DevOps, or ML Engineering (with a focus on productionizing ML at scale).
• ML Stack Mastery: Deep experience with Databricks/MLflow, Kubeflow, or AWS SageMaker.
• Technical Proficiency: Expert-level Python (including ML libraries like Scikit-Learn, PyTorch, or TensorFlow) and advanced SQL.
• Big Data Engineering: Proficient in using Spark/PySpark to process massive datasets for feature extraction and engineering.
• Orchestration: Hands-on experience with Airflow or specialized ML orchestrators to manage complex dependency graphs.
• Deployment & Infrastructure: Strong understanding of Dock

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