Machine Learning Engineer - Trust and Safety

Requirements:

• Over 5+ years of professional experience in end-to-end development of large-scale ML systems in production.
• Experience in end-to-end delivery management of projects.
• Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large-scale production systems.
• Experience using common machine learning frameworks (e. g., TensorFlow, PyTorch) and libraries (e. g., scikit-learn, NumPy, pandas).
• Deep understanding of machine learning and software engineering fundamentals.
• Basic knowledge and skills related to monitoring systems, logging, and common operations in production environments.
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders.

Preferred skills:

• Experience developing AI-based anomaly detection and content moderation systems is preferred.
• Functional development and bug-fixing skills necessary to improve system performance and reliability.
• Experience with technology such as Docker and Kubernetes.
• Experience with cloud platforms (AWS, GCP, Microsoft Azure, etc. ).
• Microservice development and operations experience with Docker and Kubernetes.
• Utilizing deep learning models/LLMs in production.
• Experience in publications at top-tier peer-reviewed conferences or journals.