About the role
We’re looking for Research Engineers to contribute to our ambitious research agenda spanning multimodal foundational models, interactive world modeling, and real-time generation. This role is highly collaborative and will touch many aspects of our broader research efforts, taking a full-stack approach across pretraining, SFT/RL post-training techniques, evaluation, and bringing models to production.
What you’ll do
Run experiments to teach world models new behaviors — action following, scene manipulation, camera control, and beyond
Develop and test new data strategies, architectural variants, and training techniques
Design evaluations that measure model capabilities, and use them to drive model improvement
Take features from research prototype to production, collaborating with product and creative teams to address application-specific gaps
Contribute across the entire stack (data pipelines, modeling, production inference) to move projects forward
What you’ll need
4+ years of experience in machine learning research or engineering
Familiarity with the architecture, training, and inference of large-scale multimodal generative models
Experience building robust data pipelines for pretraining and post-training
Comfort working across the full research stack: data, training, evaluation, and deployment
Proficiency with at least one ML framework (e.g. PyTorch, JAX) and experience with distributed training at scale
Ability to context-switch quickly and drive projects forward in a fast-moving, ambiguous environment
Excitement about building AI that simulates the world
Compensation
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.