Vision-Language Research Engineer: Data Strategy & RL

Anthropic Limited; San Francisco (CA); Hybrid; USD 350,000 - 850,000; Full time

Benefits offered by this job

Equity donation matching

Generous vacation

Parental leave

Flexible hours

Office space

Job summary

Anthropic is seeking a Research Engineer for the Vision team to own end-to-end data strategies, curate visual datasets, and evolve RL environments that power capable vision-language models.

You will collaborate with external data vendors, pretraining, RL, and product teams to translate data work into real-world knowledge capabilities and impact. This role emphasizes collaboration, experimentation, and practical engineering in a fast-moving research setting.

Qualifications

• Bachelor’s degree in a relevant field or equivalent experience.
• 7+ years of ML, computer vision, and software engineering experience.
• Experience with reinforcement learning or vision-language data tasks.
• Comfort with managing technical vendor relationships and rapid feedback loops.

Responsibilities

• Own data strategy for vision capabilities end-to-end, including evals and scalable RL environments.
• Manage external data vendors: write task specs, evaluate data quality, and iterate on rewards.
• Develop QA frameworks to detect reward hacking and ensure environment quality at scale.
• Run generalization experiments to measure data strategy impact on multimodal tasks.
• Collaborate with pretraining, RL, and product teams to align efforts.

Skills

ML & CV experience

Software engineering

Reinforcement learning

Vision-language models

Data curation

Vendor management

Education

Bachelor's degree

Job description

Anthropic is seeking a Research Engineer for the Vision team to own end-to-end data strategies, curate visual datasets, and evolve RL environments that power capable vision-language models.

You will collaborate with external data vendors, pretraining, RL, and product teams to translate data work into real-world knowledge capabilities and impact. This role emphasizes collaboration, experimentation, and practical engineering in a fast-moving research setting.