AI Machine Learning Engineer

Position: AI Machine Learning Engineer Location: Remote ( US Based ) Duration: 12 month contract + extensions Pay: up to $110/hr. Role Overview: You will design machine learning models, data pipelines, and evaluation frameworks that ensure confidentiality, integrity, availability, adversarial robustness, and content safety across annotation and model training platforms. You’ll work closely with engineers, data scientists, product, and security teams to take ML solutions from requirements to production while maintaining high standards of model security, fairness, and safety alignment. Primary Responsibilities: • Design, build, and deploy machine learning models that protect training data, annotations, and model outputs, ensuring confidentiality, integrity, and availability. • Develop defenses against prompt injection, model extraction, data leakage, and adversarial attacks, improving overall model stability and robustness. • Implement adversarial testing, stress testing, and safety evaluations to identify vulnerabilities in ML systems. • Ensure production ML systems meet product security and reliability requirements across distributed environments. Safety Data & Alignment: • Build and maintain safety data collection and safety data alignment pipelines to improve model behavior during training and evaluation. • Support ML fairness and bias detection, contributing to responsible and trustworthy AI systems. • Partner with Trust & Safety and product teams to integrate content safety enforcement into ML workflows. Platform & ML Engineering: • Design, build, and deploy machine learning models to support large‑scale data annotation platforms. • Develop models to understand user intent, interest, and content relevance while enforcing safety constraints. • Own end‑to‑end ML development: requirements, experimentation, implementation, deployment, and monitoring. • Build and support large‑scale data pipelines and CI/CD workflows for ML systems. • Perform data preprocessing, transformation, and exploratory data analysis on large, complex datasets. • Collaborate cross‑functionally to translate business, safety, and security requirements into production ML solutions. • Experiment with new modeling approaches to continuously improve model performance, safety, and reliability. Requirements: • Bachelor’s degree in Computer Science or related field (or equivalent practical experience). • Proven experience as a Machine Learning Engineer delivering production ML systems. • Strong Python skills and experience with TensorFlow, PyTorch, or scikit‑learn. • Experience with data manipulation and analysis (NumPy, pandas). • Solid understanding of ML algorithms, statistics, and data structures. • Experience with software development best practices and version control (Git). • Ability to design experiments, run A/B tests, and evaluate ML model performance in production environments. Nice to Have: • Experience with data annotation platforms or synthetic data generation. • Production experience deploying and maintaining deep learning systems at scale. • Experience with LLMs, NLP, or multimodal models. • Exposure to ML security, adversarial ML, or content moderation systems. • Strong analytical mindset and ability to work independently on