Software Jobs in Mountain View, CA (Now Hiring) — 1,096 open — Page 2

Minimum qualifications:

• Bachelor's degree in Computer Science, a related technical field or equivalent practical experience.
• 5 years of experience in technical program management.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a closely related technical field.
• Over 5 years leading cross-functional AI model programs, with expertise in large-scale distributed training pipelines and reinforcement learning.
• Proven ability to design lightweight, high-impact processes that structure fast-moving research environments without hindering team velocity.
• Highly comfortable with ambiguity; a strong communicator who builds trust and drives alignment across engineering, research, and leadership stakeholders.

About the job:

The Gemini Reinforcement learning (RL) Scaling team is at the frontier of reinforcement learning research for large language models, driving the reasoning, multimodal, and agentic capabilities that define the next generation of Gemini models.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities:

• Scope, plan, and lead execution for RL research workstreams in collaboration with tech leads, turning research hypotheses into structured roadmaps, experiment plans, and deliverable model milestones.
• Partner with engineering and infrastructure leads to manage and track experiments and compute allocations, enabling prioritization, monitoring training efficiency, and unblocking runs.
• Identify and resolve cross-functional dependencies across the RL research ecosystem (data pipelines, evaluations, distributed infrastructure) and partner teams.
• Establish reliable operational rhythms including experiment status dashboards, launch criteria, retrospectives, and milestone reviews, synthesizing complex training dynamics into actionable updates for tech leads and leadership.