New program
Runway Acceleration Program
The Runway Acceleration Program is a full-time, paid position that starts with a 3-month intensive venture dedicated to fostering engineers, propelling them into accomplished machine learning engineer roles within our organization. By merging a robust curriculum with hands-on projects under the Runway initiative, participants will experience the tangible impact of their contributions.
Ideal Candidate Qualifications
We are seeking candidates that exhibit excellence in at least one of the following areas:
- Software Development Achievements: Creation of or significant contributions to widely-recognized software systems.
- Machine Learning Implementation: High-quality open-source implementations of machine learning research papers or autonomous research projects.
- Systems Programming Proficiency: Experience in high-performance distributed systems and systems programming.
Prerequisites for Success in this Program:
- Exceptional programming aptitude and a solid grasp of systems design.
- Demonstrable passion for AI, shown through continuous self-education via reading papers and hands-on experimentation.
Program Highlights
- Tailored Mentorship: Participants are paired with seasoned researchers, with weekly check-ins to ensure steady progress.
- Evaluation & Transition: A stringent end-of-program review to assess competence, with successful candidates seamlessly transitioning to ML engineer roles within our team.
- Large Scale Access: Participants will have the opportunity of working with large scale GPU clusters for model training.
FAQ
What is the aim of the Acceleration Program?
The program is designed to transition talented engineers into machine learning engineer roles within our organization over a 3-month period, through an intensive curriculum and hands-on projects.
Who should apply for this program?
Engineers with a strong foundation in software development, a keen interest in AI and machine learning, and a desire to expand their skillset and transition into ML engineering roles should apply.
How is the mentorship structured?
Participants are paired with seasoned researchers who will provide guidance and have weekly check-ins to monitor progress, provide feedback, and ensure the continuous learning and growth of the participants.
What happens at the end of the program?
Successful candidates will transition to full-time ML engineer roles within Runway.
Is this a full-time, paid position?
Yes, you’ll be a full-time, salaried employee of Runway for the 3-month intensive before you're eligible to transition into ML engineering roles at Runway.
Meet the team
“The immediate feedback loop and push to productize new research makes Runway an extremely rewarding and unique workplace. It is incredibly gratifying seeing artists create amazing things with our latest tools and then their feedback challenges us to improve our models further. Plus, the whole company consists of just the smartest, most creative and kindest people which makes it a million times more enjoyable.”
Jonathan Granskog
“It’s inspiring to see the concentration of talent, curiosity and openness our team has. One of our strengths at Runway is how quickly we can go from an idea to prototype to production, and before we know it our users are already creating beautiful stories with it. It’s a unique place to be - and we are just getting started!”
Ritika Kalia
“I’m proud to work at a company that values artistic creativity and technical expertise equally — I think it’s the secret ingredient behind our innovation. As an engineer, it’s been thrilling to learn from and build on the cutting edge research from our research team.”
Alex Miller
“Working at Runway has been an incredibly exhilarating journey so far! We’re committed to working closely with creators to understand their needs, and formulate research problems around those needs. Within the company, there is a strong synergy between the research, engineering, and design teams. Together, we iterate quickly, without compromising on feature or code quality, openly present ideas, and share feedback to successfully push new features every week.”
Deepti Ghadiyaram
