Research Engineer, Multimodal Data

Eventual is a company focused on revolutionizing data processing for Physical AI systems. As a Research Engineer on the Visual Understanding team, you will develop and implement methods to make vast amounts of video data easily queryable and efficient for customer training needs.

Responsibilities:

• Own the visual understanding roadmap end-to-end: from picking the model family for a customer's taxonomy to landing it in production inference at corpus scale
• Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks
• Drive down per-clip annotation cost — model selection, distillation, batching, decode pipelining — so "annotate every clip in a 10K-hour corpus" stays economical
• Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version the outputs
• Partner with the dataloading and storage teams so visual understanding outputs flow into the index and on to the GPU without re-engineering
• Work directly with researchers at our partner labs — your shortest feedback loop is their next training iteration