Research Engineer, Visual Knowledge Work at Anthropic

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

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Anthropic seeks a Research Engineer to own the end‑to‑end data strategy for visual knowledge work, creating training data and reinforcement learning environments that unlock large language models’ visual reasoning. The role involves building evals, scaling RL environments, managing vendor relationships, improving QA frameworks, running generalization experiments, and collaborating with pre‑training, RL, and product teams to translate research into real‑world capabilities.

Salary

USD 350,000 - 850,000

Requirements

Skills

  • 7+ years of ML, computer vision, and software engineering experience through industry, academia, or other projects
  • 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
  • Care about 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 do the science that shows we’re all rowing in the same direction

Technologies

Machine LearningComputer VisionReinforcement LearningLarge Language ModelsVision‑Language ModelsSupervised LearningData EngineeringETLSoftware Engineering

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