Job 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. 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 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

Requirements

Must-haves

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.
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
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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gomachine learningml

Platform: apple
Effort: medium