AIML - Machine Learning Research Lead, RL Agents, MLR

Summary

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.

Description

* 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.

Minimum Qualifications

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 codebases, including open-source research frameworks or environment suites others build on

Strong engineering skills; comfortable working hands-on in large training codebases

Preferred Qualifications

Experience taking research from idea to product or production impact

Familiarity with efficiency-aware modeling: small models, mixture-of-experts, quantization, distillation, inference-cost constraints, or hardware-aware method design

Interest or background in open-endedness, evolutionary computation, curriculum or environment design, multi-agent systems, or self-improving systems

Principled or theoretical grounding in RL — representation, exploration, or optimization views of policy learning — alongside strong empirical work

Breadth across core machine learning — generative models, self-supervised learning, pre-training — and perspective on the field's longer arcs, not only its most recent methods

Experience growing other researchers, and managing researchers and engineers with heterogeneous specialties and synthesizing their work toward a common goal

Experience owning a large RL or post-training codebase

Pay & Benefits — Cupertino, California, United States

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $216,200 and $394,000, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Application Deadline

Apple accepts applications to this posting on an ongoing basis.