Runway - Member of Technical Staff, Research Engineer

United States$270k - $370k2mo ago

In Office Staff NA Artificial Intelligence Research Engineer Staff Engineer JAX

Requirements

• 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 • 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 • Working at Runway • Great things come from great teams. We’d love to hear from you. • 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.

Responsibilities

• 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

Benefits

• 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. • 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.