Research Engineer, Visual Knowledge Work at Anthropic

Hybrid - New York City, NY; San Francisco, CA; Seattle, WA

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Anthropic is seeking a Research Engineer, Visual Knowledge Work to own end-to-end data strategy and build reinforcement learning environments for visual knowledge tasks. The role involves managing vendor relationships, developing quality assurance frameworks, running experiments to improve multimodal capabilities, and collaborating closely with pretraining, RL, and product teams. The position requires strong expertise in machine learning, computer vision, and reinforcement learning, with a focus on large vision-language models and data pipeline development.

Salary

USD 350,000 - 850,000

Requirements

Skills

  • 7+ years of ML, computer vision, and software engineering experience
  • Experience with reinforcement learning, reward design, or training data curation for large language or vision-language models
  • Familiarity with the architecture, training, and operation of large vision language models
  • Comfortable managing technical vendor relationships and iterating quickly on feedback
  • Results-oriented with a bias towards flexibility and impact
  • Concern for the societal impacts of your work
  • Designing evals or benchmarks for LLMs or vision language models
  • Large-scale pretraining, SL, and RL on language models
  • Deep learning research on images, video, or other modalities
  • Developing complex agentic systems using LLMs
  • Large-scale ETL and data pipeline development

Responsibilities

  • Own the data strategy for vision capabilities end-to-end, from building evals and scaling RL environments
  • Manage technical relationships with external data vendors, including writing task specifications, evaluating visual data and annotation quality, and iterating on reward design
  • Develop and improve QA frameworks that catch reward hacking and ensure environment quality at scale
  • Run generalization experiments to measure how data strategy changes improve multimodal capabilities on held-out evaluations
  • Partner with pretraining, RL, and product teams, and conduct the science that aligns all teams

Technologies

Machine LearningComputer VisionReinforcement LearningLarge Language ModelsVision-Language ModelsEvaluation frameworksData pipelinesETLClaude

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