onsite
Multi-Modal Machine Learning Engineer all levels - Trust and Safety
ML Engineer
Lead the design and deployment of multi-modal machine learning models for content classification, leveraging deep learning and graph neural networks to enhance trust and safety across platforms.
About the role
Key Responsibilities
- Develop and train multi-modal deep learning models for content classification, integrating text, image, and audio data streams.
- Design and implement graph neural network architectures to capture relational patterns in user-generated content.
- Collaborate with data scientists to create data-driven strategies that improve model accuracy and reduce bias.
- Deploy models into production environments, ensuring scalability, reliability, and compliance with safety standards.
- Monitor model performance, conduct A/B testing, and iterate on feature engineering and hyperparameter tuning.
Requirements
- Proven experience in machine learning and deep learning, with a strong portfolio of production-ready models.
- Hands‑on expertise with graph neural networks and related frameworks (e.g., PyTorch Geometric, DGL).
- Solid background in data engineering, including data pipelines and feature stores.
- Strong programming skills in Python and familiarity with cloud platforms (AWS, GCP, or Azure).
- Excellent problem‑solving abilities and a passion for building safe, trustworthy AI systems.
Skills
machine learningdeep learning