Remote Research Engineer — Data Foundations

Workman Labs; United States; Hybrid; USD 270,000 - 370,000; Full time

Job summary

Runway is hiring a research engineer for Data Foundations, building large-scale video and image data pipelines that its generative models train on. You will design and operate end-to-end data systems to enable model development.

Responsibilities include creating multimodal datasets, running experiments to understand data impact, and collaborating with product teams to translate needs into data strategies. A strong background in ML and data-centric approaches is essential.

Qualifications

• 4+ years of experience in machine learning.
• Experience with large multimodal datasets (video/image).
• Strong intuition of data quality impact on models.
• Experience across data analysis, dataset creation, training, evaluation.
• Familiarity with PyTorch or JAX and distributed tools.

Responsibilities

• Design multimodal datasets to teach world models.
• Run controlled training experiments to study data effects on performance.
• Build large-scale pipelines for synthetic data generation and QC.
• Define evaluations/benchmarks to measure model improvements.
• Collaborate with product and creative teams on data strategy.

Skills

ML engineering

Multimodal datasets

Data-centric ML

Distributed compute

Tools

PyTorch

JAX

Ray

Kubernetes

Job description

Runway is hiring a research engineer for Data Foundations, building the large-scale video and image data pipelines that its generative models train on.

$270,000 – $370,000 / yr

Location

Remote (US)

Remote

Seniority

—

Compared with 15 other Research roles: Common across similar roles Unique to this role

What you'll do

Responsibilities

• 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 Common
• Run controlled training experiments to understand how data composition drives model performance across tasks and domains Common
• Build and operate large-scale pipelines for synthetic data generation, filtering, and quality control Common
• 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 you'll need

Experience

• 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) Common
• 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 Common
• 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 Unique