Research Engineer -- Vision and Vision-Language Models

End-to-end pretraining and post-training of vision-language models

Large-scale vision and multimodal training runs

Vision and multimodal dataset sourcing, generation, curation, processing, and evaluation

Reinforcement learning and other post-training methodologies for vision-language models

Computer use, visual agents, and other multi-turn agentic vision tasks

Architecture, data, and training methodology ablations and improvements

Performance optimization of our training and data processing stacks

Strong research taste and intuition, with the ability to take ambiguous problems from conception to experimentation and execution

Strong implementation and prototyping ability; you can turn ideas into experiments quickly

A high rate of learning and an ability to rapidly become effective in unfamiliar technical areas

Strong general machine learning intuition and an interest in understanding the full model development stack

The ability to work well with others in a high-paced research setting

Excellent communication and collaboration skills, and the ability to work effectively across both research and engineering implementation at scale

Experience working with vision, vision-language, video, or other multimodal models or datasets is highly valuable

Experience training vision-language models is a plus, but not required

Experience with large-scale model pretraining, post-training, reinforcement learning, or related methodologies

Experience sourcing, generating, or curating vision datasets, including synthetic data generation

Familiarity with image processing tools, libraries, or pipelines

Experience with computer use agents, visual agents, GUI understanding, or other multi-turn multimodal tasks is a plus

Experience with frontend development, web frameworks, UI/UX implementation, or mobile development is valuable, particularly for computer use and interface-understanding tasks

Experience training models on large-scale, multi-node GPU clusters

Strong grasp of experimental methodology for running rigorous ablations and hypothesis testing

Understanding of large-scale, highly parallel data processing pipelines

Proficiency with PyTorch and Python

Strong ability to dive into large pre-existing codebases and rapidly get up to speed

Published machine learning research in well-respected venues is a plus

Postgraduate study in a scientific or technical field such as Computer Science, EE/EECS, Mathematics, Physics, or Machine Learning is a plus, but we care more about demonstrated ability and trajectory than credentials