Vision RL Data Engineer for Multimodal AI

Anthropic; New York (NY); Hybrid; USD 350,000 - 850,000; Full time

Benefits offered by this job

Competitive compensation

Equity donation matching

Generous vacation

Parental leave

Flexible working hours

Job summary

Anthropic is seeking a Research Engineer for the Vision team to own end-to-end data strategies for visual capabilities. You will design and scale RL environments, evaluate data quality with vendors, and ensure rewards drive real-world vision-language performance.

This role blends applied research with hands-on data work, collaborating with pretraining, RL, and product teams to deliver scalable, impactful capabilities. Location-based hybrid work is expected.

Qualifications

• 7+ years of ML, computer vision, and software engineering experience.
• Experience with reinforcement learning or training data curation for LLMs/vision models.
• Familiar with large vision-language models and their architectures.

Responsibilities

• Own the data strategy for vision capabilities end-to-end, including evals and scaling RL environments.
• Manage technical relationships with external data vendors and design task specifications.
• Develop QA frameworks to catch 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 translate environments into real-world capabilities.

Skills

ML and CV experience

Reinforcement learning

Software engineering

Vendor management

Data curation

Education

Bachelor’s degree or equivalent

Job description

Anthropic is seeking a Research Engineer for the Vision team to own end-to-end data strategies for visual capabilities. You will design and scale RL environments, evaluate data quality with vendors, and ensure rewards drive real-world vision-language performance.

This role blends applied research with hands-on data work, collaborating with pretraining, RL, and product teams to deliver scalable, impactful capabilities. Location-based hybrid work is expected.