Engineering Manager, RL Engineering - Menlo Ventures Management, L.P

About the role:

Anthropic's RL Engineering team builds the systems, allowing large-scale distributed reinforcement learning with language models. As manager of the team, you'll support a team of machine learning and distributed systems experts with the goal of making these systems highly efficient, supporting fast iteration on model development, and continuously evolving the infrastructure to incorporate new research advances.

Our reinforcement learning system sits at the intersection of almost every technical group at Anthropic. You'll work with research teams to incorporate their innovations into our production finetuning pipeline, product teams to help us iterate quickly on customer-oriented model improvements, and infrastructure teams to make sure our training runs are as efficient and reliable as possible.

About Anthropic:

Anthropic is an AI safety and research company working to build reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our customers and society as a whole. Our interdisciplinary team has experience across ML, engineering, physics, policy, business, and product.

Responsibilities:

• Prioritize the team's work in collaboration with the technical lead, research teams, and product teams to support fast iteration on research projects and training runs.
• Design processes (e.g., postmortem review, incident response, on-call rotations) that help the team operate effectively.
• Coach and support your reports to understand and pursue their professional growth.
• Run the team's recruiting efforts efficiently, ensuring we can grow as quickly as we need through a period of rapid growth.

You may be a good fit if you:

• Believe that advanced AI systems could have a transformative effect on the world and are interested in helping make sure that transformation goes well
• Are an experienced manager (at least 1 year) and actively enjoy people management
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level) to be effective

Strong candidates may also have:

• Experience working with large language models or reinforcement learning
• Experience doing research in any domain or experience working with research teams, especially as part of a "research to production" pipeline
• Strong people management experience: Coaching, performance evaluation, mentorship, career development
• Strong project management skills: Prioritization, communicating across team/org boundaries
• Experience recruiting for your team: Predicting staffing needs, designing interview loops, evaluating candidates, and closing them