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Machine Learning Solutions Engineer
2 months ago
At Electronic Arts, we're pushing the boundaries of innovation in the gaming industry. Our Game Production Solutions group is a new division within Creative Technologies, dedicated to building cutting-edge solutions for our customers - the game teams and creators. Our mission is to empower game creators to make a significant impact with our technology.
Your Role with UsWe're working on exciting projects related to generative AI and Machine Learning, focusing on Generative AI for game assets. Our platform is designed to power Generative AI for game creators across EA, using powerful models fine-tuned and adapted for asset creation. Performance, ease of use, and security are our top priorities.
As a key member of our team, you'll be responsible for deploying AI/ML solutions for our game creators, taking the solution from development to production, ensuring security compliance, implementing scaling technology, and more. You'll work closely with our Software Engineers to manage the workflows for asset pipelines and partner with them to implement scaling technology.
This role demands a strong understanding of traditional DevOps and infrastructure responsibilities, as well as MLOps. You'll take a hands-on approach to managing cloud infrastructure, working with our Technical Director to oversee DevOps and MLOps in our group.
What We're Looking For- You have a passion for DevOps, MLOps, and cloud infrastructure, with a strong desire to forge a career path in these areas.
- You enjoy being a hands-on contributor, with experience in architecture, design, and implementation.
- You're willing to step beyond your comfort zone and take on new challenges.
- You thrive in a versatile team of Software Engineers and Technical Artists.
- Manage large-scale data sets: Develop solutions for collecting, storing, cleaning, and labelling training data (game assets).
- Build end-to-end machine learning systems: Including data collection/labelling, training pipelines, model deployment, and monitoring.
- Automation: Automate the CI/CD process for model training.
- Deployment: Deploy services and models to cloud infrastructure at scale.
- Monitoring: Monitor ongoing execution and performance.
- Documentation: Create documentation on processes and technologies.
- Collaboration: You'll work with partners across the company to ensure compliance with infrastructure, security, and development standards.
- 5+ years of experience as an engineer focused on DevOps, MLOps, or a similar domain.
- Proficiency in software engineering best practices: version control, testing, code review.
- Proficient in using Python, Spark SQL to query large datasets.
- Expertise in ML frameworks like PyTorch, TensorFlow.
- Experience working with cloud services at scale (one of AWS, Azure, or Google) using IAC tools such as Terraform.
- Experience integrating / working with MLOps pipelines and products like Databricks or similar.
- Comfortable working in a remote or hybrid environment.
- Willing to travel to meet with team members and partners in person.
- Bachelor's degree in Computer Science (or related technical field) or equivalent practical experience.
