Machine Learning Engineer, GenAI Applied ML

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for both public and private evaluations.

About This Role

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.

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.

You will:

• 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
  
  

Ideally you’d have:

• 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
  
  

Nice to have:

• Hands-on experience designing or scaling trust & safety detection systems
• Familiarity with data quality pipelines or contributor platform risk analysis
• Contributions to open-source LLM fine-tuning efforts or internal LLM alignment projects
• Research or published work in top ML venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)