Lead RL & Agentic AI Research—On-Device & Tools

Socket.dev; Cupertino (CA); On-site; USD 220,000 - 320,000; Full time

Job summary

Socket.dev is seeking a hands-on research lead to guide reinforcement learning and post-training initiatives for agentic AI, while managing a small team of senior researchers across RL, tool-calling, synthetic environments, and multimodal action models.

You will shape the infrastructure, training, runtime, and evaluation strategies for interactive agents, and remain actively involved in experiments, coding, and publishing.

Qualifications

• PhD in machine learning or related field, or equivalent research experience.
• 7-10+ years of research experience beyond PhD in industry or academia.
• Strong track record in RL and/or post-training of large models, evidenced by publications, open-source contributions, or shipped systems.
• Leadership experience coordinating researchers and engineers over multiple years.

Responsibilities

• Lead RL and post-training research for agentic capabilities, including reward, preference optimization, and verifier design.
• Build and own synthetic data and task-generation pipelines and the environments and benchmarks that accompany them.
• Drive codebases and infrastructure for the core RL research effort and coordinate with partner teams.
• Manage and mentor a small team (3–4) of senior researchers and research engineers, shaping a shared direction while preserving bottom-up work.
• Stay hands-on: run experiments, write code, and contribute directly to the team's core technical problems.
• Bridge post-training research with deployment considerations for on-device and hybrid compute environments.
• Collaborate across the organization on environment-agent co-optimization, self-improvement, and long-context agents.
• Publish in top venues and engage with the broader research community.

Skills

RL research

Leadership

ML infrastructure

Hands-on engineering

Education

PhD or equivalent

Tools

PyTorch

RL frameworks

Job description

Socket.dev is seeking a hands-on research lead to guide reinforcement learning and post-training initiatives for agentic AI, while managing a small team of senior researchers across RL, tool-calling, synthetic environments, and multimodal action models.

You will shape the infrastructure, training, runtime, and evaluation strategies for interactive agents, and remain actively involved in experiments, coding, and publishing.