Software Engineer, Perception
July 27
🔄 Hybrid – San Francisco
Description
• Applying machine learning to solve challenging perception problems for autonomous systems (e.g. object detection, semantic segmentation, instance segmentation, dense depth, optical flow, tracking, etc.). • Driving the architecture, deployment, and performance characterization of our deep learning models. • Refining and optimizing models for low-latency inference on embedded hardware. • Designing and building cloud-based training and labeling pipelines. • Collaborating with the hardware and embedded teams on sensor selection and vehicle packaging given safety requirements. • Writing performant, well-tested software, and improving code quality of the entire Autonomy team through code and design reviews.
Requirements
• 5+ years of experience in software development for problems involving computer vision, machine learning, and robotic perception techniques. • Foundational understanding of deep learning: model layer design, loss function intuition, training best practices. • Experience handling large datasets efficiently and organizing them for training and evaluation. • Experience curating synthetic and real-world image datasets for training. • Strong proficiency in modern C++ and Python and experience writing efficient algorithms for resource-constrained embedded systems. • Ability to thrive in a fast-moving, collaborative, small team environment with lots of ownership. • Excellent analytical, communication, and documentation skills with demonstrated ability to collaborate with interdisciplinary stakeholders outside of Autonomy. • An eagerness to get your hands dirty by testing your code on real robots at real customer farms (gives “field testing” a whole new meaning!).
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