Research Engineer, Data Foundations

Job summary

Responsibilities

Building general world models — systems that understand and simulate reality across tasks, modalities, and domains — demands training data that is as rich and varied as the real world itself.

• Design multimodal, multitask datasets that teach world models new capabilities — deciding what data to collect, generate, or curate and measuring its effect on model behavior
• Run controlled training experiments to understand how data composition drives model performance across tasks and domains
• Build and operate large-scale pipelines for synthetic data generation, filtering, and quality control
• Define evaluations and benchmarks that measure whether our models are actually improving at the things that matter
• Partner with product and creative teams to translate target behaviors and capabilities into concrete data strategies

What they're looking for

• 4+ years of experience in machine learning, bonus points for data-centric approaches
• Experience with large multimodal datasets and generative models (video, image, or multimodal)
• Deep intuition for how data composition and quality translate to model capabilities
• Comfort working across the full research stack: data analysis, dataset creation, model training, evaluation, and back again
• Proficiency with at least one ML framework (e.g. PyTorch, JAX) and distributed compute tools (e.g. Ray, Kubernetes)

Tech stack

• PyTorch
• JAX
• Ray
• Kubernetes