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 Data Engine
Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.
About our Fraud team
The Fraud team works on preventing violations to our terms and service. They protect our platform against potential fraud, cheating, and abuse that are emerging in the GenAI era. The team works at the intersection of machine learning, analytics, and security. We’re looking for Machine Learning Engineers to join this team. In this role, you'll be given the opportunity to build cutting-edge models to make an impact in the AI industry, and meaningfully drive millions of dollars in revenue. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.
Responsibilities:
- Develop state-of-the-art models to detect and combat fraud, cheating, and spamming in the GenAI ecosystem. Your work will play a crucial role in maintaining the trust and reliability of our AI solutions.
- Design and implement features that elevate model performance, ensuring our solutions stay ahead of emerging threats and challenges.
- Define and drive key performance indicators that directly influence business success.
- Assess technical tradeoffs with a critical eye, making decisions that balance innovation, efficiency, and practicality.
- Uphold coding standards and architectural principles by crafting clean, well-documented, and modular code.
- Actively engage in code reviews, offering constructive feedback, and actively contributing to continuous improvement efforts across the engineering department.
Requirements:
- Strong understanding of machine learning approaches and algorithms
- Able to build out ML pipeline from feature generation to model deployment
- Able to prioritize duties and work well on your own
- Ability to work with both internal and external partners
- Skilled at solving open ambiguous problems
- Strong collaboration skills