Machine Learning Research Scientist/ Engineer, Agents

Position Overview

Scale AI is a leading AI data foundry dedicated to accelerating AI applications. The ACE team, part of our Research organization, focuses on developing autonomous agents that interact with diverse external environments—from code repositories and GUI interfaces to browsers and more. In this role, you will study critical data types for building intelligent agents, guide our data strategy to drive innovation, contribute to impactful research publications, and collaborate with cross-functional teams to turn research into scalable, real‐world solutions.

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role.

Key Responsibilities

• Study and analyze data types essential for state-of-the-art agents.
• Explore the data landscape to advance intelligent, adaptable AI agents.
• Guide data strategy to drive innovation at Scale AI.
• Contribute to impactful research publications on agents.
• Collaborate with customer researchers and engineering teams to translate research into scalable solutions.

Required Qualifications

• Practical experience working with LLMs using frameworks like Pytorch, Jax, or TensorFlow, with the ability to quickly translate research into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM).
• At least three years of experience addressing sophisticated ML problems in research or product development.
• Strong written and verbal communication skills and the ability to work cross-functionally.

Preferred Qualifications

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke fine-tuning projects using frameworks such as Pytorch or Jax.
• Publications and experience in building applications and evaluations related to AI agents (e.g., tool-use, text2SQL, browser agents, coding agents, GUI agents).
• Experience with agent frameworks like OpenHands, Swarm, or LangGraph.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH.
• Experience working with cloud technologies (e.g., AWS or GCP) for developing machine learning models.