Research Engineer, Visual Knowledge Work

Details

About the role

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.

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

Requirements

• 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

Strong candidates may also have experience with

• 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

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas. If we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.