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Anthropic · San Francisco/New York City/Seattle · Hybrid
Research Engineer, Visual Knowledge Work
1/20/2026
Description
We're looking for a research engineer who believes that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. On the Vision team, you'll own the end-to-end process of creating training data and RL environments targeting visual knowledge work: identifying long-horizon and vision-heavy tasks, building evals, designing rewards, and scaling data. This is a unique role that combines applied research with hands-on data work. It's also highly collaborative — you'll partner with external vendors, pretraining, RL, and product teams to make sure the environments you build translate into real-world knowledge work capabilities.
What you'll do:
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
Qualifications
Have 7+ years of ML, computer vision, and software engineering experience through industry, academia, or other projects
Have experience with reinforcement learning, reward design, or training data curation for large language or vision-language models
Are familiar with the architecture, training, and operation of large vision language models
Are comfortable managing technical vendor relationships and iterating quickly on feedback
Are results-oriented, with a bias towards flexibility and impact
Care about the societal impacts of your work
Nice to have
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
Writing a vendor-facing specification for a new family of visual RL training tasks, then iterating with the vendor on coverage, quality, and reward design
Running experiments to determine ideal training datamixes and parameters for a synthetically generated vision dataset
Finetuning Claude to maximize its performance using a particular set of agent tools/skills