About the Team

Come help us build the world's most reliable on-demand, logistics engine for last-mile delivery! We're looking for an experienced senior machine learning engineer to help us develop cutting-edge Computer Vision models that guard DoorDash's delivery quality.

About the Role

We are seeking a highly motivated and experienced Applied Machine Learning expert with a specialization in Computer Vision and multi-modal systems to join our team. In this role, you will be responsible for the design, implementation, and validation of innovative ML solutions that identify potential delivery defects at an early stage—whether it's identifying visual anomalies in packages or addressing errors in delivery logistics. By integrating real-time visual data with complementary data sources, your work will enable proactive intervention, ensuring prompt recovery and enhanced delivery quality. Leveraging our robust data and ML infrastructure, you'll build production-level algorithms that not only extract accurate signals from visual feeds but also incorporate multi-modal inputs to address complex, real-world challenges. The ideal candidate is passionate about solving end-user problems, excels in collaborative, multidisciplinary teams, and is a drive to transform the delivery experience by preventing defects before they impact the customer.

You’re excited about this opportunity because you will… Develop production-level machine learning solutions that address key vision challenges for our dashers. This includes applying advanced techniques—such as entity recognition, attribute extraction, object detection, segmentation, metric learning, image classification, multi-modal fusion. Collaborate with cross-functional leaders across engineering, product, and business strategy to help shape a product roadmap driven by machine learning, accelerating the growth of a multi-billion-dollar retail delivery ecosystem. Explore and harness diverse data sources, leveraging intuitive models and fostering a culture of flexible experimentation. Your goal will be to continuously improve the delivery experiences, delivering solutions that are not only effective but also scalable and user-centric. Drive impact and enjoy ample room for both personal and professional growth. This includes opportunities for deep technical development and leadership as you work within a growing team that is actively unlocking new markets for the company. Innovate and experiment with cutting-edge technologies and methodologies in AI and ML. You’ll have the freedom to explore emerging technologies, test new ideas, and drive technical innovation in the computer vision space, directly shaping the future of our delivery solutions

You can find out more on our ML blog here

We’re excited about you because you have… 5+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. You have successfully shipped ML solutions to production and understand the nuances of transitioning models from development to real-world environments. Deep expertise in applied Computer Vision, with hands-on experience solving challenging vision-related problems and implementing solutions that drive customer value. Your knowledge extends to solving complex tasks such as object detection, classification, and segmentation. Strong machine learning and programming skills, particularly in Python, with experience in key ML frameworks such as PyTorch, TensorFlow, or Spark. You’re able to implement scalable models and optimize performance for production systems. You must be located near or are willing to relocate to one of our engineering hubs which includes: San Francisco, Sunnyvale and Seattle. M.S., or PhD. in Computer Vision, Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative fields is a plus. Familiarity with causal inference and experimentation techniques, enabling you to design and evaluate experiments that validate the effectiveness of ML models and ensure the product’s continuous improvement

 

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