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

Requirements

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)
Excitement about building AI that simulates the world

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata analysisdataset creationmodel trainingevaluationmultimodal datasetsgenerative modelsdata compositiondata qualityAI simulation

Soft Skills

collaborationcommunicationproblem-solvingcreativityanalytical thinking