Engineering Manager, Research Productivity at Anthropic

Hybrid - San Francisco, CA, USA

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Anthropic’s Research Tools team builds systems that support our large-scale, distributed finetuning runs and improve the productivity of researchers. As a manager, you’ll support a team of machine learning and distributed systems experts to make these systems and tools highly efficient, support fast iteration on model development and research, and evolve the infrastructure continuously to incorporate new research advances.

Salary

USD 405,000 - 850,000

Requirements

Skills

  • Belief that advanced AI systems could have a transformative effect on the world
  • Experienced manager (at least 2 years) and actively enjoys people management
  • Quick study: ability to understand a large number of complex technical systems at a high level
  • 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
  • Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • Field relevant to the role as demonstrated through coursework, training, or professional experience

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

Technologies

machine learningdistributed systemsfinetuning pipeline

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