Scale AI is looking to accelerate the development of AI applications, specifically focusing on generative AI, defense applications, and autonomous vehicles. The role aims to build and deploy machine learning systems that power internal and external customer use cases within Scale's GenAI platform, ensuring the usability, reliability, and value of their ML workflows.
Requirements
- 3+ years of experience building and deploying ML models in production environments
- Experience delivering ML solutions that serve real-world user or customer needs
- 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)
- Hands-on experience rapidly prototyping and iterating on ML systems with changing requirements
- Familiarity with data quality pipelines or internal evaluation frameworks
Responsibilities
- Design and deploy machine learning models to power core customer-facing and internal GenAI features
- Build real-time and batch ML systems that analyze structured and unstructured signals
- Combine traditional ML techniques with LLMs and neural networks to improve task performance and reliability
- Create robust evaluation frameworks and iterate quickly based on performance and feedback
- Collaborate closely with product and engineering teams to embed ML systems into production workflows and infrastructure
Other
- Excellent communication skills and a proven ability to work cross-functionally across product, ops, and engineering
- The base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$176,000—$220,000 USD
