Strategic Overview
Research Engineer, Data Foundations at Runway is a dev opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as 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….
The role is listed for Worldwide with remote working arrangement, giving candidates the practical context needed to assess fit before applying. Relevant signals in the listing include Research.
Key information at a glance
About This Role
We are building AI to simulate the world through merging art and science. We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error…
Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you.
*Titles posted to our careers page are updated regularly to best advertise the role and expected seniority, but internally we have a flat titling system with two types of titles: one for individual contributors and one for people managers.
Key Responsibilities & Objectives
- 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
Required Qualifications & Candidate Blueprint
- 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)
Salary & Benefits
Published compensation: $270K – $370K
- 4+ years of experience in machine learning, bonus points for data-centric approaches
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
We are building AI to simulate the world through merging art and science.
We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world.
World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached.
Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you.
*Titles posted to our careers page are updated regularly to best advertise the role and expected seniority, but internally we have a flat titling system with two types of titles: one for individual contributors and one for people managers.
About the role
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. We’re looking for Research Engineers to own the data behind our models: what they learn from, how well they learn it, and what new capabilities that unlocks. You will design datasets, run modeling experiments, and build the infrastructure to generate and curate data at scale — directly shaping what our models can do, with applications ranging from creative tools to robotics.
What you'll do
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 you'll need
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
Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide.
There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.
Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates.
Working at Runway
Great things come from great teams. We’d love to hear from you.
We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. So regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply.
More about Runway
Universal World Simulator
GWM-1
Gen-4.5
General World Models
Robotics SDK
Conversational Real-time Agents
Runway Studios
We're excited to be recognized as a best place to work:
Crain's | InHerSight | BuiltIn NYC | INC
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This dev opening is a useful opportunity for candidates whose experience matches Research. Its strongest career signal is the scope of the work itself — particularly 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 — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground Research, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.