As an Applied AI Engineer on the Autonomous Agents team, you will be pivotal in bridging the gap between the rapidly evolving space of agent applications and frameworks, agentic LLM capabilities, and the data required to advance them.
Design agent data programs that improve model performance through supervised fine-tuning (SFT) and reinforcement learning (RL)
Build new agent data types and pipelines that enable agentic data collection using multiple environments, like code repos, browsers, and computers
Meet regularly with customers to understand their modeling requirements and product objectives
Develop agentic frameworks, tools, and verifiers to evaluate model capabilities for frontier general agent tasks
Implement popular open source agent libraries and benchmarks on proprietary datasets and models
Build agent applications that enable and automate key aspects of our data pipelines and evaluations
A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to build solutions
Strong AI & engineering background: Master's degree/or equivalent experience in Computer Science, Machine Learning, AI, or a related field
Strong background in deep learning, LLM, and data-centric AI methodologies
2-3 years of practical experience building AI applications for real-world use cases.
Previous experience in a customer facing role
Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)
Experience building AI applications on the modern GenAI stack, using commercial or open-source LLMs and common SDKs like the OpenAI API
Experience building agents that use tools, generate structured output, and interact with environments
Familiarity with agent data and benchmarks, such as SWE-Bench for SWE agents, and OS-World for Computer-use agents.