Durable
Fernando Nobre has a strong background in robotics and perception, with experience in various companies and research institutions. Fernando currently serves as the CTO at Durable since 2022. Prior to that, they worked at Amazon from 2019 to 2022, with roles such as Applied Science Manager and Senior Perception Scientist. Fernando also worked at CANVAS Technology, which was later acquired by Amazon, from 2018 to 2019 as a Perception Scientist. Fernando's earlier experience includes working as a Graduate Research Assistant at the University of Colorado Boulder from 2014 to 2018, where they focused on visual-inertial SLAM and self-calibration for autonomous robots. Fernando also gained internship experience at the Toyota Research Institute in 2017 and Zoox in 2015. Before that, they worked as a Product Development Engineer at Embraer from 2013 to 2014, specializing in software and airborne electronic hardware processes. Fernando started their career as a Computer Engineer at Taqtile Brasil in 2011 and had internships at Iteris Consultoria e Software in 2010 and 2009 as a .NET Developer. Overall, Fernando Nobre has a diverse range of experience in the robotics industry, with a focus on perception and autonomous systems.
Fernando Nobre completed their education with a Doctor of Philosophy - PhD in Computer Science from the University of Colorado Boulder, from 2014 to 2018. Prior to that, they obtained a Master's Degree in Computer Science from the same university between 2014 and 2016. Fernando Nobre's earlier education includes a Bachelor's Degree in Electrical and Computer Engineering with a focus on Computer Engineering from USP - Universidade de São Paulo, which they completed from 2006 to 2010.
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Durable
We’re on a mission to transform access to custom software using explainable AI capable of human-level reasoning and dialogue. We envision a future where custom, flexible, and durable software is democratized and accessible to everyone. We are a VC-funded startup founded by repeat founders, building a product in a new category. Realizing this vision requires AI that reasons over and continuously learns an unbounded and customized knowledge base. It requires AI that, when given a task, determines missing information, asks clarifying questions, and highlights assumptions. It requires AI that explains its reasoning and validates its answers. We don’t see evidence that the current paradigm of ever-larger deep learning language models can realize this vision. So our approach combines the strengths of deep learning in dealing with noisy and ambiguous data with the strengths of symbolic AI in explainable reasoning and data-efficient learning. We believe that this neuro-symbolic flavor of AI will enable more useful and reliable applications in the long term. If, like us, you are skeptical of the status quo and are excited to develop the next chapter of AI in a product-focused team, reach out to us or check out our open positions.