AIML - Machine Learning Research Lead, RL Agents, MLR
Hands-on research lead at Apple's MLR group directing reinforcement learning and post-training work for agentic AI (tool calling, coding, computer use, long-horizon tasks), while managing a small team of senior researchers and running experiments personally. Core focus: synthetic environments/data pipelines, RL infrastructure, and methods suited to Apple's on-device and hybrid cloud deployment.
- Compensation
- Not disclosed
- Location
- Cupertino
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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 (publications, open-source contributions, or shipped systems)
- Leadership experience: setting and defending a research direction over multiple years and directing others' work (formal management not required)
- Experience owning ML infrastructure, frameworks and codebases, including open-source research frameworks or environment suites
- Strong engineering skills; comfortable working hands-on in large training codebases
Nice to have
- 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)
- Breadth across core machine learning: generative models, self-supervised learning, pre-training
- Experience growing other researchers and managing researchers/engineers with heterogeneous specialties
- Experience owning a large RL or post-training codebase
Full description
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.
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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.
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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.
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Drive codebases and infrastructure for the core RL research effort and help engage partner teams to use and co-develop the framework.
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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.
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Stay hands-on: run experiments, write code, and contribute directly to the team's most important technical problems.
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Connect post-training research to efficiency and deployment: what works under on-device and hybrid compute constraints, and how method design interacts with hardware.