Sense’s mission is to reduce global carbon emissions by making homes smart and efficient. We make it easier for people to take care of their homes and to actively participate in a cleaner, more resilient future.
To accomplish this mission, we are building an AI and automation platform for home energy infrastructure. Come join our team and ship a great consumer product built on our unique machine learning technology. We have a wide range of interesting technical challenges, a deep stack that spans embedded, native mobile, web, and the cloud, and a team of highly curious, collaborative engineers who enjoy solving hard problems together. If the idea of having a significant positive impact on the product (and climate) sounds exciting to you, let's talk!
The ideal candidate will have direct experience with some of the following:
Onsite in our Cambridge, MA office is preferred, but we're open to discussion if you're well set up for remote embedded development. All of our EE is done in-house, so you'll be working closely with the hardware team to make our hardware sing. You will also be working directly with our product team to develop new user-facing features. To be successful in this role, you should love working from the very low-level all the way up to the user experience, and everything in between.
We are a team of generalists, which means that we believe your previous experience is valuable, even if it's not directly related to this role's requirements. Impress us with your ability to thrive in an environment where you are challenged to learn new skills and solve difficult problems.
Sense supports a diverse and inclusive workplace where we learn from each other. We welcome candidates with backgrounds that are traditionally underrepresented in tech, and we strive to foster an engaging, respectful and supportive community where everyone feels empowered to do their best work. Sense is committed to being an equal opportunity employer.
Sense is a growing VC-backed startup with an experienced leadership team and revolutionary machine learning technology.