AIML - Machine Learning Research Lead, RL Agents, MLR

AIML - Machine Learning Research Lead, RL Agents, MLR

Cupertino, California, United States Machine Learning and AI

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.

• Lead research on RL and post‑training for agentic capabilities: reward, preference optimization, and verifier design, training recipes, and evaluation for tool calling, coding, and multi‑step interactive tasks.
• Build and own synthetic data and task‑generation pipelines — generating diverse, verifiable tasks and environments, along with the interactive environments and benchmarks that go with them, and the curricula that turn them into capable agents.
• Drive codebases and infrastructure for the core RL research effort and help engage partner teams to use and co‑develop the framework.
• Manage and mentor a small team (3–4) of senior researchers and research engineers with distinct specialties, shaping a shared research direction while protecting room for bottom‑up, idea‑driven work.
• Stay hands‑on: run experiments, write code, and contribute directly to the team's most important technical problems.
• Connect post‑training research to efficiency and deployment: what works under on‑device and hybrid compute constraints, and how method design interacts with hardware.
• Collaborate across the organization on adjacent directions, including methods for environment and agent co‑optimization, self‑improvement, world models used as planners or policies, and personalized long‑context agents.
• Publish in top venues and engage with the broader research community.

• PhD in machine learning or a related field, or equivalent research experience
• 7-10+ years of research experience beyond PhD in industry or as an academic research lead
• Strong track record in RL and/or post‑training of large models, demonstrated through publications, open‑source contributions, or shipped systems
• Leadership experience: setting and defending a research direction over multiple years, and directing others' work — through direct reports, PhD students, postdocs, or sustained project teams. Formal management experience is welcome but not required
• Experience owning ML infrastructure, frameworks and…