Senior Machine Learning Engineer

Company Description

Polycreek is dedicated to protecting children from online exploitation, abuse, grooming, and the spread of harmful content. The organization builds advanced tools that support earlier detection, faster reporting, and more effective intervention across digital platforms. Polycreek collaborates with parents, organizations, and online services to help create safer digital environments for young users. Team members contribute to a mission-driven culture focused on reducing harm and strengthening protection for children in an increasingly connected world.

Role Description

This full-time remote Senior Machine Learning Engineer role focuses on designing, building, and deploying machine learning models that detect online risks and harmful behavior patterns involving children. Day-to-day responsibilities include developing and optimizing algorithms for pattern recognition, training and evaluating neural network architectures, and working with large-scale datasets to improve detection accuracy and system robustness. The engineer will collaborate closely with product, data, and engineering teams to translate safety requirements into scalable technical solutions and production-ready pipelines. The role also involves conducting experimental research, reviewing model performance and fairness, and implementing best practices for reliability, security, and privacy in ML systems.

Qualifications

• Strong foundation in Computer Science and Algorithms, with experience designing efficient, scalable systems and data structures.
• Expertise in Pattern Recognition and Neural Networks, including practical experience with deep learning frameworks and model deployment.
• Proficiency in Statistics, with ability to design experiments, interpret model metrics, and apply statistical methods to real-world data.
• Advanced programming skills in languages such as Python, along with experience in ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Experience building end-to-end machine learning pipelines, including data preprocessing, feature engineering, training, evaluation, and monitoring.
• Background in trust and safety, cybersecurity, or online risk detection is highly beneficial; prior work on content moderation or abuse prevention is a plus.
• Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field preferred; equivalent practical experience considered.
• Ability to work independently in a remote environment, collaborate in cross-functional teams, and communicate complex technical concepts clearly.