Research Scientist/Engineer, GenMedia Safety Post-Training TLM, DeepMind

Role Overview

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities

• Define the architectural strategy for placing safety interventions across multimodal foundation models, balancing shared approaches with bespoke solutions.
• Advance generalizable post-training methods across media types to ensure seamless alignment as new prompting and conditioning capabilities emerge.
• Shape intrinsic model behavior to refuse severe harms while fulfilling borderline requests with high generation quality and controllability.
• Partner with cross-functional teams to distill serving-time classifiers into training rewards and expand the safety and quality trade-off frontier.
• Manage a team of 10 or more researchers and engineers, modeling scientific excellence and building a collaborative culture.

Requirements & Qualifications

Minimum qualifications:

• PhD or equivalent practical research experience.
• 5 years of experience in a technical leadership role setting direction and guiding project workstreams.
• 5 years of experience in a people management, supervision, or team leadership role.
• 5 years of experience in machine learning, specifically working with large language models (LLMs), multimodal foundation models, or reinforcement learning (RL), along with safety and alignment specialization (e.g., model alignment, AI safety, or post-training techniques like Supervised Fine-Tuning and reward modeling).
• One or more peer-reviewed scientific publications (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, CoRL) or public preprints (arXiv).

Preferred qualifications:

• 6 years of demonstrated experience initiating, scoping, and executing independent research agendas or leading collaborative academic projects.
• Experience in core strategic focus areas: LLMs, post training, reasoning and reinforcement learning, agents, multimodal perception/vision, robotics, audio/speech, safety and alignment or scientific AI (health, materials, fusion).
• Track record of bridging research breakthroughs with product engineering or external scientific/policy stakeholders.
• Sustained record of first author and senior author scientific publications in premier peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, CVPR, SIGGRAPH, CoRL) or journals (e.g., Science, Nature, PNAS).

Application Instructions

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