Staff Machine Learning Engineer, Simulation
Wing SecuritySaaS Security company
Palo Alto, United States$274,000 - $292,000Lead
CapitalG
Insight Partners
S Capital VC
Oren Yunger
Cyberstarts VC
Crystal Huang
Data & AI
About the role
TL;DR
Lead the development of simulation models for Wing's autonomous drone delivery system.
- •Wing is looking for a Staff Machine Learning Engineer, Simulation to join our Simulation team.
- •This role is hybrid based in Palo Alto.
- •Key Responsibilities Lead the design, development and deployment of world models and generative systems for realistic and controllable sensor generation for large-scale simulation at Wing’s autonomous system.
- •Develop generative pipelines to build high-fidelity synthetic datasets, leveraging SOTA multimodal models, diffusion techniques and world-models to simulate complex 4D environments.
- •Partner with research teams across Alphabet to integrate advanced modeling techniques.
- •Champion sim-to-real efforts, using domain adaptation and transfer learning techniques to ensure our simulated models faithfully capture the behaviors of physical, on-vehicle systems.
- •Apply VLMs to enhance the understanding and controllability of our world simulation products.
- •Play a pivotal role in shaping the broader AI infrastructure across the organization, establishing best practices, optimizing workflow management for large-scale training, and championing foundational AI initiatives.
- •Requirements 12+ years of experience developing and designing machine learning applications, autonomous systems, or simulation platforms.
- •B.S, M.S., or Ph.D. degree or equivalent practical experience in Computer Science, Machine Learning, Robotics, or a related field.
- •Demonstrated ability to lead technical ML projects of significant scope and complexity, driving initiatives from research to production-ready solutions.
- •Deep expertise in 3D World Modeling or 3D computer vision.
- •Familiarity with 3D reconstruction and rendering techniques (e.g., 3D Gaussian Splatting).
- •In-depth knowledge of generative AI, predictive world models, autoregressive models, or self-supervised learning from multi-modal sensor streams.
- •Hands-on experience with sim-to-real transfer, domain adaptation, and world models.
- •Experience developing testing frameworks and evaluating ML models for edge cases and rare events in complex systems.
Required skills
PythonTensorFlowPyTorchscikit-learn
Domain expertise
logistics
Benefits & perks
equity, bonus
Tech stack
PythonTensorFlowPyTorchscikit-learnAirflowKubernetesAWSGoogle Cloud