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

• Health, Dental & Vision Coverage: Our health plans give you the flexibility to select the right coverage for you and eligible family members through a variety of plan options
• Easy to use 401(K): Plan and invest for the future with a 401(k) via Guideline. Scale’s 401(k) plan provides you an opportunity to defer compensation for your long-term savings
• Wellness Fund: We care about the physical, mental, and emotional wellbeing of all Scaliens. Our $100/month wellness stipend can be used for gym memberships, acupuncture, meditation apps, and so much more
• Virtual Social Activities: Being remote has not stopped us from hosting fun virtual events. From trivia night to candle making, we ensure employees are fostering connections & building strong relationships
• Learning & Development: We know how important career growth is for Scaliens, so we offer a $500/year L&D stipend to help support continued development throughout your journey
• Flexible Work Hours: Flexible hours allow you to work when you are most productive. You can work with your manager to best plan your daily work schedule
• Generous Paid Time Off: Enjoy time to travel or plan a staycation. We encourage employees to take time off to recharge and prevent burnout. We have a flexible PTO policy where each employee is afforded the flexibility to take planned time-off as needed
• Commuter Benefits: Set aside pre-tax dollars to use on qualified transportation expenses to help ease your commute
• Parental Leave: Balancing work and family is essential, and Scale understands the importance of having adequate leave policies in place to promote a healthy home and work life

Total funding: $1.6bn

When training AI models, lack of access to large scale, high quality, labelled data is often the barrier preventing machine learning teams from building something great. In fact, labelled data is even more essential than the algorithms themselves. Scale AI is solving this by providing easy access through developer APIs to a huge variety of labelled data sets covering a large range of areas within computer vision and natural language processing.