Staff ML Engineer, Simulation — Drone World Modeling

Wing; San Francisco (CA); Hybrid; USD 274,000 - 292,000; Full time

Job summary

Wing is seeking a Staff Machine Learning Engineer, Simulation to join our Simulation team in a hybrid role based in Palo Alto. This role focuses on integrating advanced ML into Wing’s simulation stack used for autonomous aircraft.

You will lead world modeling efforts, build synthetic data pipelines, and collaborate with Alphabet researchers to improve simulator realism and scale AI infrastructure across the organization.

Qualifications

• Requires advanced degree or equivalent practical experience in CS/ML/Robotics.
• Proven ability to lead large ML projects from research to production.
• Deep expertise in 3D world modeling or 3D computer vision.
• Experience with sim-to-real transfer and domain adaptation.
• Experience with testing ML models for edge cases in complex systems.

Responsibilities

• Lead design, development and deployment of world models and generative systems for realistic sensor generation in large-scale simulation.
• Develop generative pipelines to build high-fidelity synthetic datasets using multimodal models and diffusion techniques.
• Collaborate with Alphabet researchers to integrate modeling techniques.
• Advance sim-to-real efforts with domain adaptation and transfer learning.
• Apply vision-language models to enhance world simulation products.
• Shape AI infrastructure, optimize large-scale training workflows, and champion foundational AI initiatives.

Skills

12+ years experience ML/simulation

Lead ML projects

3D world modeling

3D computer vision

Generative AI

Sim-to-real transfer

Testing ML in complex systems

Education

Ph.D. or equivalent in CS/ML/Robotics

Tools

3D Gaussian Splatting

Job description

Wing is seeking a Staff Machine Learning Engineer, Simulation to join our Simulation team in a hybrid role based in Palo Alto. This role focuses on integrating advanced ML into Wing’s simulation stack used for autonomous aircraft.

You will lead world modeling efforts, build synthetic data pipelines, and collaborate with Alphabet researchers to improve simulator realism and scale AI infrastructure across the organization.