About the role:
Anthropic’s Applied Finetuning team builds on Anthropic’s Finetuning research to make Anthropic’s business and products successful. As a Research Engineering Manager, you will lead a team of researchers and research engineers who directly train the flagship models we launch to the public via Claude.AI and our API. You will lead a team designing and iterating on state-of-the-art finetuning techniques, such as Constitutional AI and RLHF, to train our production Claude models. Your team will implement new algorithms, run experiments on data mixes, design evaluations, and improve our production model training pipeline.
Responsibilities:
- Lead research and engineering efforts to train production models through post-training techniques
- Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
- Manage day-to-day execution of the team's work
- Prioritize the team’s work and manage projects to support fast iteration on research projects and training runs
- Coach and support your reports in understanding, and pursuing, their professional growth
- Maintain a deep understanding of the team's technical work and its implications for AI safety
You may be a good fit if you:
- Have 3-5 years of management experience in a research or technical environment
- Have a background in machine learning, AI, or a related technical field
- Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
- Excel at building strong relationships with stakeholders at all levels
- Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
- Have experience managing teams through periods of rapid growth and change
- Are comfortable working in a fast-paced, research-driven environment where priorities may shift quickly
- 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 of abstraction) to be effective
