As a team, we are working to define and solve the hardest problems in computer vision and AI. Our roots are in research, which means at our core, we value learning, intellectual curiosity, and self-starters. We are proud to maintain a high engineering bar as our team scales. The software we write is portable across many platforms with little to no change.
As a startup, we never shoehorn people into fixed roles. At Xnor, there is no hard line between machine learning and engineering. We expect our engineers to understand how deep learning works and our research scientists to be able to code. This shared understanding results in deep collaborations that enable our team to rapidly develop novel models and optimized infrastructures of the highest production quality.
My day to day life is to learn about the state-of-the-art, come up with new tricks, and bring them to real end user products. At Xnor I spend my time on problems that matter (as opposed to making people click on ads). In my work, I face all kind of challenges from low level algorithmic and resource management challenges, to high level engineering design and reusability challenges. The challenges and the impact that I make have made this a dream a job for me.
Machine Learning Engineer
Being part of Xnor’s hardware team gives me a lot of opportunities to influence the direction of the team which feels amazing. Also, with the power of Xnor AI technology, I can now turn my hardware ideas into cool end-to-end solutions that solve real world problems today.
Saman Naderiparizi, PhD,
Our machine learning and engineering teams work in concert to optimize our applications end-to-end resulting in best in class performance and accuracy.
Carlo C del Mundo,
We're writing C++ that is fast and resource-efficient. It has to work on a range of platforms under demanding conditions, so we put a lot of energy into crafting quality code that is robust and flexible.
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