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Perception Intern (Summer 2027)

Zipline · 2027 Internships

Where
South San Francisco, California, USA
Posted
Aug 20

As an intern in the Droid Perception team, you will dive into ML model experimentation, evaluation and integration to push the boundaries of our current onboard and offboard perception systems.

Our distinctive challenges require groundbreaking approaches, and you'll have the chance to see your ideas transition from concept to real-world application. In a fast-paced and collaborative environment, you'll look at current research in the field and use that to propose novel solutions to our pressing perception challenges.

Join us, and be at the frontier of shaping the future of autonomous deliveries!

What you'd do

  • You must have completed the second year of your undergraduate studies. Master’s and PhD students are also eligible
  • Good theoretical understanding of 3D computer vision and various camera models
  • Hands-on expertise in and in-depth understanding of one or more of the following areas: multi-view depth estimation, semantic segmentation, Gaussian splatting, generative modeling, BEV-style models, VLAs, VLMs, model optimization
  • Deep understanding of state-of-the-art research and trends in one or more of the following areas: multi-view depth estimation, semantic segmentation, Gaussian splatting, generative modeling, BEV-style models, VLAs, VLMs, model optimization
  • Proficiency with deep learning frameworks like PyTorch, JAX, or TensorFlow
  • Experience writing high-quality code in a production environment, e.g., at a previous internship
  • Ability to rapidly experiment, iterate, and adapt to new findings in a dynamic environment

About Zipline · 2027 Internships

Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably.