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.