You will apply machine learning and large language model techniques—such as fine-tuning, reinforcement learning, and preference optimization—to solve real customer problems and improve advertiser outcomes at scale. You will work closely with senior scientists and engineers to experiment with new methods, run large-scale evaluations, and bring research ideas into production. You will be hands-on in implementing models, analyzing data, and building components that make our guidance agents more context-aware, reliable, and effective. Most importantly, you will work backwards from advertiser needs, contributing to customer-facing products that help advertisers create, optimize, and grow their campaigns.Key job responsibilities
- Contribute to the design and development of agents that guide advertisers across conversational and non-conversational experiences.
- Implement and experiment with model and agent optimization techniques such as supervised fine-tuning and instruction tuning under the guidance of senior scientists.
- Support dataset curation and tooling for model customization and preference optimization (e.g., MCP pipelines).
- Build and maintain components of evaluation pipelines for agent workflows, including benchmark setup, automated test creation, and analysis of reasoning quality.
- Prototype and validate elements of agentic architectures (e.g., CoT, ReAct, or ToT) to improve planning, reasoning, and tool use.
- Conduct experiments, analyze performance, and communicate insights to drive iterative improvements to models and agents.- Stay current with emerging methods in LLMs, reinforcement learning, and agentic AI, and apply them to real-world advertiser scenarios.
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
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