Summary: The Machine Learning Engineer, Gen AI will design and deploy machine learning models, combine traditional ML with LLMs, and build real-time and batch ML systems. Highlights: 1. Design and deploy machine learning models for GenAI features 2. Combine traditional ML with LLMs and neural networks 3. Build real-time and batch Machine Learning systems Scale AI, Inc. in San Francisco, CA seeks Machine Learning Engineer, Gen AI Responsibilities: The Machine Learning Engineer, Gen Ai is responsible for designing and deploying machine learning models to power core customer-facing and internal GenAI features; combining traditional Machine Learning techniques with LLMs and neural networks to improve task performance and reliability; Building real-time and batch Machine Learning systems that analyze structured and unstructured signals; working with cloud technology tac (e.g., AWS and/or GCP) building Machine Learning systems in microservice architectures and developing machine learning models in a cloud environment; Creating robust evaluation frameworks and iterate quickly based on performance and feedback; work with engineering, product, and operations teams to clarify specification, define practical implementation plans, and rapidly iterate toward effective deployed solutions; Collaborating closely with product and engineering teams to embed Machine Learning systems into production workflows and infrastructure; assist in quality prototyping, developing new deep learning models targeted at multi-model content understanding problems, and building a large hybrid human-machine system in service of machine learning pipelines for Scale AI’s products and industry-leading customers. Employee may be stationed anywhere in the continental U.S., but will report to headquarters in San Francisco, California. Salary Range: $240,000 - $280,000 Education: Master’s degree in Computer Science, Software Engineering, Mathematics, or closely related engineering field (or foreign equivalent). Requirements: 2 years of experience in the job offered or related; 2 years of experience working with Machine Learning and deep learning frameworks such as scikit-learn, PyTorch, TensorFlow, or JAX; 2 years of experience working with data quality pipelines or internal evaluation frameworks; 2 years of experience applying foundation models for structured downstream tasks; and 2 years of experience with Machine Learning algorithms, data modeling, and evaluation. Qualified applicants must email resume to apply @scale.com quoting job #MLEGA26.