Wing is looking to advance its autonomous drone delivery system by leveraging deep learning to ensure aircraft navigate the world with precision and safety, as part of its mission to create the preferred means of delivery for the planet.
Requirements
- 8+ years of experience in ML, deep learning, or computer vision, ideally within robotics or autonomous systems.
- Proficiency in Python and/or C++.
- Hands-on experience with JAX, PyTorch, or TensorFlow.
- Deep understanding of Linear Algebra and modern CV architectures (CNNs, Transformers, etc.).
- Domain Expertise: Proven track record in one or more of the following: Multi-view Geometry & 3D Reconstruction, Monocular or Stereo Depth Estimation, Object detection, localization, Semantic Segmentation, Local and global image features
Responsibilities
- Design & Deploy: Research, build and deploy highly reliable ML models optimized for resource-constrained, real-time environments.
- Scale Research: Utilize Wing’s massive datasets to develop and refine complex models that push the boundaries of environmental understanding.
- Innovate: Apply state-of-the-art CV foundation models to solve real-world robotics challenges.
- Collaborate: Partner across hardware, software, and flight-testing teams to take features from concept to production-ready flight code.
- Mentor: Educate and drive ML/AI adoption at Wing.
Other
- MS or PhD in Computer Science, Robotics, Electrical Engineering, or equivalent industry experience leading production-grade ML systems.
- The ability to drive complex features independently and a history of delivering simple, well-thought-out solutions to business-critical problems.
- Publications at top-tier Robotics/CV/ML conferences such as CVPR, ICCV, ECCV, ICML, ICRA, RSS, NeurIPS.
- 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.
