Machine Learning Engineer, Fraud

Scale Ai - New York, NY

Full-time - Mid Level

New York, NY

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

This role will lead the development of trust & safety models to detect fraud and violations on Scale AI's platform at a large scale. The ideal candidate will have experience in the industry working on trust & safety to detect misuse via account and behavioral signals, contributing to the future of AI by ensuring high-quality data delivery to foundation model builders.

Responsibilities

• Lead the development of trust & safety models to detect fraud and violations on the platform.
• Utilize account and behavioral signals to identify misuse.
• Deploy machine learning services to production in a microservices cloud environment.
• Test detection systems in the presence of extreme class imbalance.
• Ensure contributors on the platform are trustworthy and high quality.

Requirements

• Practical experience deploying machine learning models to production in a microservices cloud environment.
• Familiarity with large language models (LLMs) and proficiency in frameworks like scikit-learn, Pytorch, Jax, or Tensorflow.
• At least three years of experience addressing sophisticated machine learning problems in a research or product development setting.
• Strong written and verbal communication skills and the ability to operate cross-functionally.
• Experience working with cloud technology stack (e.g., AWS or GCP) and developing machine learning models in a cloud environment.

Nice-to-haves

• Hands-on production experience developing models for detecting trust & safety violations.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM).
• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.

Benefits

• Comprehensive health, dental and vision coverage
• Retirement benefits
• Learning and development stipend
• Generous PTO
• Commuter stipend