Studio: Scopely
Job Opening: Machine Learning Engineer
Location: Remote
Type: Full-Time
Responsibilities:
- You will focus on unleashing ML capabilities for LiveOps teams that work on different gaming platforms
- The day-to-day activities will involve designing, developing, and researching ML systems, models, and schemes, contributing to technical plans, and discussing the workflows and requirements with stakeholders
- We are responsible for building a sophisticated suite of LiveOps tools driven by Machine Learning (ML) automation and decision-making. This will implicitly change how MidCore manages its games in perpetuity. Apply advanced statistical / ML methods to large, complex data sets
- Transform data science prototypes into production-usable ML models while ensuring ML models remain performant over time
- Build and maintain cloud-native infrastructure for hyper-scale ML models serving
- Work with other developers to implement tools to empower our Live Operators and Game Design teams to create new and exciting game-play experiences and LiveOps Automation tool suite support & troubleshooting
- Contribute to technical plans and discuss the workflows and requirements with stakeholders
Qualifications & Skills:
- A deep understanding of ML technologies development and best practices
- Highly experienced in delivering production quality, highly performant ML platforms
- Experience with machine Learning Frameworks (PyTorch, MXNet, TensorFlow, etc.) and model serving frameworks (Seldon Core, TorchServe, TensorFlow Serving, etc.)
- Very strong coding ability, and experience in development in at least one mainstream programming language (Python, Java, C++, etc.) and with at least one major public cloud provider (AWS / Azure / GCP)
- Experience writing and maintaining automated unit, integration, and end-to-end tests
Bonus Points
- Experience building, managing, and maintaining MLOps platforms
- Experience in at least one of the following: Amazon Redshift, Google BigQuery, or Azure SQL Data Warehouse
- Experience building recommendation systems
- You’ve mentored less experienced team members
