We are looking for a hands-on research lead to drive our work on reinforcement learning and post-training for agentic AI, and to manage a small team of senior researchers working on related problems in RL, agentic tool-calling, synthetic environment generation, model scaling, and multimodal action models. You will help set direction for how we develop infrastructure, training, runtime and evaluation procedures for interactive agents — tool calling, coding, computer use, and long-horizon tasks.
This role sits inside a research organization pursuing first-principles approaches to core AI problems: generative foundation models across modalities (text, images, graphs, scientific and engineering data), vision-language modeling and implicit world modeling, self-supervised learning, and search and evolutionary methods for optimizing both agents and the environments they learn in. A distinctive part of our agenda is designing methods that fit Apple's deployment reality — on-device and hybrid (device plus private cloud) execution, co-designed with current and future hardware — and that take advantage of what this ecosystem uniquely enables, such as deeply personalized, long-context agentic experiences. We aim for both field-changing research and direct impact on Apple products and internal engineering processes.
MLR is a research group first. Management here is about spreading the load of running a team, not stepping away from the work — everyone, including leads, stays hands-on. We support continued engagement with the academic community: publishing, conference service, student collaboration, and internships.