Machine Learning Research Engineer, GenAI Applied ML

Scale AI•San Francisco, CA

About The Position

This role will lead the development of machine learning systems powering internal and external customer use cases across Scale’s GenAI platform. As a core part of our Generative AI data engine, these systems are critical to ensuring the usability, reliability, and value of our end-to-end ML workflows. You will build scalable ML services that incorporate 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 clarify specifications, define practical implementation plans, and rapidly iterate toward effective deployed solutions. If you’re excited about solving real-world ML problems, deploying iteratively, and collaborating closely with cross-functional teams to deliver value fast, we’d love to hear from you.

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)
Excellent communication skills and a proven ability to work cross-functionally across product, ops, and engineering

Nice To Haves

Hands-on experience rapidly prototyping and iterating on ML systems with changing requirements
Familiarity with data quality pipelines or internal evaluation frameworks
Contributions to open-source LLM fine-tuning efforts or internal LLM alignment projects
Research or published work in top ML venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)

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