We are Hiring!

We are a small and focused team dedicated to building the next generation compression technology with deep learning. We believe future compression will leverage machine learning to be content-aware, task-aware, and recipient-aware. We are reinventing the way people store, transmit and represent digital media. Our technology has the potential to disrupt multiple industry verticals. We make things small, but we think big!

Regardless of your role, we are looking for people who have strong communication skills, are eager to learn and teach others, and enjoy working in a fast-paced, collaborative environment. If you are excited to join the founding team of WaveOne and you feel you have what it takes, please reach out!

Regardless of your specific role, you will:

• Build cutting edge technologies with an experienced team
• Maximize your impact by learning how to prioritize what is most important
• Collaborate with a small team of people who care about your success
• Work in an environment that promotes work-life balance (writing high-quality code and doing innovative research requires both hard work and rest)
• Observe and have the chance to influence the company strategy, growth, and culture
• Get premium health and dental coverage and free lunch with the team

Location and availability: We only hire full-time employees that (once the apocalypse is over) are willing to work on-site from our offices in Palo Alto or Vancouver, Canada. We do not hire consultants or interns at this time.

Note on title qualifiers: We are interested in hiring both junior and senior people. Your seniority as well as your responsibilities will be reflected in your offer. However, our policy at WaveOne is to not use title qualifiers, such as Senior, Principal, Architect, etc. Title qualifiers are not reflective of our culture.

Research Scientist

Compression is one of the most exciting areas of deep learning research today because it is both practically grounded and ripe for creative innovation. On one hand, advances that lead to better compression rates translate into real dollars and cents (e.g. reduced transmission costs). On the other hand, the ability to do compression with deep learning blows the door wide open to a host of creative innovations that are prohibitively difficult to approach using traditional methods. For instance, more perceptually-relevant metrics are more readily optimized, and multimodal features can be learned (e.g. optical flow + residuals). Furthermore, neural network architectures and model efficiency optimization schemes must be adapted to compression problems.