Machine Learning Engineering Intern, Summer 2027
About Wing:
Wing offers drone delivery as a safe, fast, and sustainable solution for last mile logistics. Consumer appetites for on-demand services are increasing, but current delivery methods are inefficient, costly, and contribute to road accidents and air pollution. Wing’s fleet of highly automated delivery drones can transport small packages directly from businesses to homes on-demand, in minutes. We design, build, and operate our aircraft, and offer drone delivery services on two continents. Our technology is designed to be easy to integrate into existing delivery and logistics networks, offering a scalable drone delivery solution for a broad range of businesses. Wing is a part of Google's parent company, Alphabet, and our mission is to create the preferred means of delivery for the planet. If you're ready to do the greatest work of your life, come join us.
About the Program:
Join Wing's 12-week paid summer internship! This is a hybrid, in-person program based in Palo Alto, CA, with start dates in May or June. Eligible candidates must be a current student enrolled in a degree program and intend to return to the program after the completion of the summer internship.
About the Opportunity:
Wing is looking for a Machine Learning Engineer Intern to join our Perception team based in Palo Alto, CA. You will be at the forefront of advancing our autonomous drone delivery system, leveraging deep learning to ensure our aircraft navigate the world with precision and safety. At Wing, you aren’t just building models in a vacuum; you have access to tens of thousands of hours of flight data from a diverse sensor suite
In this role, you will:
- Help us track towards our goal of building unified and/or generalized Perception models that helps Wing aircraft systems gracefully handle a wide variety of edge cases they experience when operating in the real world
- Analyze real-world data, coming up with new ideas on how to improve our existing Perception pipelines with state-of-the-art foundational and end-to-end geometric models
- Gain experience with leveraging large amounts of diverse, real-world data from a fleet of thousands of robots to build ML models that solve real problems.
- Make a measurable impact on Wing's ability to scale our operations more rapidly, at the forefront of the rapidly growing drone delivery industry.
- Be a part of a dynamic and collaborative team.
What You’ll Need:
- Currently pursuing a Master's, or PhD degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Computer Vision, Robotics, Electrical Engineering, or related field, graduating between December 2027 and August 2028
- Strong examples of previous engineering experience from past internships and/or academic or extracurricular projects.
- Strong grasp of basic physics and engineering fundamentals in Computer vision
- Strong grasp of modern computer vision technologies such as self-supervised Vision Transformer foundation model, Vision Language Models, VLAs, and hands-on experience in at-least one of those domains
- Experience with C++ and/or Python.
The US base salary range for this internship position is the salary range below + benefits. Wing’s salary ranges are determined by role, level, and location. Your recruiter can share more about the specific salary range for your location during the hiring process.
Wing is an equal opportunity employer and it is Wing's policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity. Employment at Wing is based solely on a person's merit and qualifications directly related to professional competence. Wing does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law.
If you have a need that requires accommodation during the interview process due to a disability or special need, please let us know by completing our Candidate Accommodations Request Form.
