Key skills
CloudDockerKubernetesPythonAIMachine LearningMLMLOps
About this role
Role Overview
- Building and deploying production ML models for within our content and product ecosystem
- Designing scalable ML infrastructure and pipelines that handle massive media datasets
- Implementing inference systems for content optimization across multiple verticals
- Fine-tuning and deploying multimodal AI systems using MLOps best practices
- Collaborating with data science teams to transition research models into production-ready systems
- Optimizing model performance for cost efficiency while maintaining accuracy and speed requirements
- Integrating ML capabilities into existing platforms and building APIs for seamless model consumption
Requirements
- Degree in Computer Science, Machine Learning, Mathematics, Engineering, or related technical field
- 3+ years of hands-on ML engineering experience building production systems at Big Tech companies, high-growth startups, or media/entertainment platforms
- Expert-level proficiency in Python, ML frameworks, and cloud platforms
- Extensive experience with MLOps tools and practices including Docker, Kubernetes, model versioning, and monitoring systems
- Proven track record deploying and scaling ML models in production environments with high availability requirements
- Self-directed approach with ability to architect complex systems independently while collaborating across technical teams
- You thrive in a fast-paced and performance-oriented environment
- Colleagues would describe you as hard-working, ambitious and persistent
- You're obsessed with music, video or social media
Tech Stack
- Cloud
- Docker
- Kubernetes
- Python
Benefits
- We pay competitive salaries and make you an owner of the business with equity.
- We work remotely to give you complete freedom over your life, while meeting regularly around the world for global offsites where we strategize, bond, and push boundaries together.