As a member of the AI model team, you will drive innovation in post-training methodologies, with a special focus on agentic behaviors and tool use. Your work will refine pre-trained models so that they not only deliver enhanced intelligence and domain-specific capabilities, but also learn to reason, plan, and autonomously invoke external tools to solve real-world, multi-step tasks and applications on edge devices (i.e., smartphones). You will work on a wide spectrum of systems, ranging from streamlined, resource-efficient agents that run on limited hardware to complex multi-modal architectures integrating text, images, and audio, all optimized for tool-augmented decision-making. We expect you to have deep expertise in large language model architectures and substantial experience in post-training for agentic workflows, including tool use fine-tuning, function calling, and reinforcement learning from feedback on multi-turn interactions. You will adopt a hands-on, research-driven approach to developing, testing, and implementing new post-training algorithms that unlock goal-directed behavior, self-correction, and reliable tool invocation.
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Responsibilities
- Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results.
- Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration.
- Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure.
- Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment.
- Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training.
- Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models.
- Closely liaise with research, engineering, and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.
Requirements
- Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) preferred with a strong publication record in top-tier AI conferences.
- Experience with multimodal post-training workflows and data pipelines, particularly for agentic systems and tool use.
- Hands-on experience applying post-training at scale using distributed training frameworks (e.g., multi-node GPU environments).
- Demonstrated experience improving model capabilities in areas such as reasoning, tool use, and multi-agent coordination that achieve SOTA results.
- Proven track record of open-source contributions related to agentic systems or tool use (code, datasets, or models) on platforms such as GitHub or Hugging Face.
- Publications at leading AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, ECCV).
