Machine Learning Engineer

2K is looking for an experienced and skillful Machine Learning Engineer to join a rapidly growing team at the groundbreaking edge of creativity in the central Data & Analytics Organization. We bring AI, machine learning, statistics, operations research, and economics to the design, operation, and optimization of 2K’s games. This covers the application of algorithmic solutions to areas such as matchmaking, in-game experience personalization, in-game economy, content, and fraud.

Our vision at 2K is to create a diverse and inclusion environment to Come As You Are and feel equipped to do your best work! We are dedicated to promoting diversity, multiculturalism, and equality in all that we do. Our communities are focused on increased access and personal growth, and their greatness depends on a diversity of race, gender, sexual orientation, religion, ethnicity, national origin, and perspective. We're an equal opportunity employer, and we're excited to build the future of co-living with the world's most hardworking and passionate people.

This person will have to be an excellent software engineer! On top of that, the ideal individual will have a track record of deploying and productizing machine learning models, especially experience with deployment as prediction services. You should also have exposure and understanding of the machine learning lifecycle, including different types and tasks of ML, algorithms, ML workflow, and common modeling/computing frameworks. We are looking for a meticulous, creative problem solver; a self-starter with the passion and enthusiasm to get results for meaningful change, meet deadlines, and build whatever is vital along the way is essential for this role! Capable and comfortable making architectural choices when working on sophisticated solutions consisting of multiple components.

• Collaborate with machine learning scientists, data engineers, backend engineers, and game studios to deploy ML models as production-grade intelligent decision services and integrate with larger systems or products.
• Build robust MLOps practices for the team and larger data science group: advocate and foster engineering standard methodologies within the data science community. Emphasize reliability and quality.
• Design and quickly prototype AI/ML-powered products and services for various applications, including recommenders, matchmaking, cheat/toxicity intervention, and economy balancing.
• Work closely with game studio devs and central tech to plan and execute integrated solutions of AI/ML applications into games in time for launch.

This person is not expected to take prototype code and rewrite it for production but instead, build the platform to enable multiple data/ML scientists to deploy their code upon. They’ll work with our data engineering and DevOps departments to do so (not building a stack in a silo). They’ll also be encouraged to be a senior collaborator for data/ML scientists for coding standard methodologies and more sophisticated (e.g., real-time) algorithm development and deployment.

• Advanced degree (Master+) in Computer Science, Computer Engineering, Electrical Engineering, or related STEM fields.
• Proficient in Python, plus at least one high-performance system programming language (C, C++, Java, Rust, …). Skilled with object-oriented and functional programming paradigms
• Familiarity with common ML tasks, including supervised, unsupervised, and with reinforcement learning as a plus. Understanding of commonly used ML algorithms (traditional ML and deep learning)
• Knowledge of stack architecture skills, experience writing infra as code and deploying
• Proven understanding of machine learning modeling/computation frameworks such as scikit-learn, PyTorch, Tensorflow, or Spark ML
• Experience with big data technologies such as Relational and NoSQL databases, Hadoop, or Apache Spark
• Readiness for collaboration with machine learning model developers, data engineers, and game developers

Bonus Points

• Recommender systems, search engines, information retrieval, matchmaking, or reinforcement learning technologies
• Cloud computing platforms such as AWS, GCP, or Azure. Containers, container orchestration, serverless deployment, microservice architecture
• Stream data processing and related tools such as Apache Kafka, Kinesis, Spark Streaming
• Major game engines such as Unreal or Unity