Machine Learning Engineer, Scale AI

• If you’re excited about solving complex detection problems at scale, combining LLMs with structured ML approaches, and protecting the integrity of AI training data, we’d love to hear from you
• 3+ years of experience building and deploying ML models in production environments
• Experience with trust & safety, fraud detection, abuse prevention, or adversarial modeling in a real-world setting
• Proficiency in ML and deep learning frameworks such as scikit-learn, PyTorch, TensorFlow, or JAX
• Familiarity with LLMs and experience applying foundation models for structured downstream tasks
• Strong software engineering fundamentals and experience building ML systems in microservice architectures (e.g., using AWS or GCP)
• Excellent communication skills and a proven ability to work cross-functionally

Desirable

• This role will lead the development of machine learning systems to detect fraud, abuse, and trust violations across Scale’s contributor platform
• As a core part of our Generative AI data engine, these systems are critical to ensuring the quality, safety, and reliability of the data used to train and evaluate frontier models
• You will build scalable ML services that analyze behavioral and content signals, incorporating both classical models and advanced LLM-based techniques
• This is a high-impact, product-focused role where you’ll collaborate across engineering, product, and operations teams to proactively surface misuse, defend against adversarial behavior, and ensure the long-term health of our human-in-the-loop data workflows
• Design and deploy machine learning models to detect fraud, quality issues, and violations in large-scale contributor workflows
• Build real-time and batch detection systems that evaluate account, behavioral, and content-level signals
• Combine traditional ML techniques with LLMs and neural networks to improve detection capabilities and reduce false positives
• Create robust evaluation frameworks and actively tune for extremely imbalanced detection scenarios
• Collaborate closely with product and engineering teams to embed detection systems into contributor-facing workflows and backend infrastructure

• PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants