About Us

Moonware is a venture-backed startup enabling next generation air travel with automated and sustainable airfields. We’re building an integrated ecosystem of AI software and smart vehicles that efficiently connect ground crew, aircraft, and ground support equipment to efficiently service flights. 

Our long-term vision is to handle aircraft autonomously from touchdown to takeoff, providing seamless aircraft turnarounds for a streamlined airport experience.

Moonware was founded with the belief that multimodal air transportation will become the prime mover of people and goods during the 21st century. From eVTOLs that will ferry passengers between cities and suburban areas, to supersonic aircraft shaving travel time between continents, we are ushering in a new air and space age that will further globalize humanity.

Airports and the hubs uniting these modes of transport will need to evolve to meet this new modern demand for air travel. In spite of the current advancements in aircraft technology, the infrastructure and ground processes that will enable these new aerial ecosystems to happen are largely outdated.

We are builders hailing from Tesla, Waymo, NASA, Google, Uber and Corvette, leveraging years of product development experience in aerospace, automotive, and robotics to revolutionize the airfields of tomorrow.

Software Team

Moonware’s software team is building HALO, the OS for aviation ground handling operations.

HALO is an AI-powered Ground Traffic Control platform designed to streamline aircraft turnaround management, seamlessly coordinating ramp crew and ground support equipment (GSE) to service flights on time. HALO’s core optimizer fuses flight information, GSE locations and crew tasks to create optimal service missions. Position trackers, instrumented on GSE, provide vehicle location information that is used to define app-specific features. 

Crew & GSE are scheduled & dispatched through the mobile app to service aircraft according to real-time flight schedule changes, with a smart routing feature that navigates ground crew drivers to their destination on the airfield, saving time. 

Real-time task allocation and dynamic vehicle routing are examples of features that take place in the cloud, effectively replacing the current use of radio and paper. These time-sensitive operations are migrated over to digital infrastructures that can process the large datasets necessary for task & equipment allocation along with their inherent complexities.

This approach enables us to collect large amounts of data that is virtually absent on ground vehicle whereabouts and movements, interactions between ground handling agents, and service time punctuality. Furthermore, by collecting ground data from different airport hubs, we can build robust machine learning models that take into account airfield traffic patterns and airport-specific constraints. We can then use this data to inform where the greatest bottlenecks in ground operations are occurring and how these processes can be augmented by ‘last-mile’ ramp services with autonomous GSE. This is what we call laying the bridge to airfield autonomy.

About the Role

We’re seeking an accomplished Senior Machine Learning Engineer to be a crucial player in our technology-driven journey. As a Senior Machine Learning Engineer, you’ll lead the development of machine learning models that drive intelligent decision-making in our ecosystem. Collaborating closely with cross-functional teams, you’ll contribute to reshaping the aviation industry through data-driven solutions.

Responsibilities:

  • Design, develop, and deploy machine learning models to enhance the capabilities of our HALO and ATLAS platform.
  • Collaborate with software engineers, data scientists, and domain experts to identify opportunities for applying machine learning techniques.
  • Lead the end-to-end machine learning pipeline, from data collection and preprocessing to model training and deployment.
  • Optimize models for performance, scalability, and real-time operation.
  • Apply software methodology to take an ML algorithm from a prototype to a final production quality deployment.
  • Participate in code reviews, provide technical insights, and contribute to continuous improvement of development processes.
  • Stay updated with emerging technologies and best practices in machine learning.

Requirements:

  • 3+ years of experience in machine learning or related roles.
  • Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
  • Extensive experience with designing, training, and deploying machine learning models.
  • Experience building models for speech recognition and/or natural language processing.
  • Familiarity with data preprocessing, feature engineering, and model evaluation.
  • Knowledge of version control systems (e.g., Git) and agile development methodologies.
  • Excellent problem-solving skills and attention to detail.
  • Excellent communication and collaboration skills.

This Role Might Be For You If:

  • You are passionate about driving innovation through machine learning in a dynamic environment.
  • Problem-solving and collaboration are at the core of your work ethic.
  • You thrive in a fast-paced startup environment where your contributions have a tangible impact.
  • You’re excited about shaping the future of aviation technology through data-driven solutions.
  • You enjoy staying updated on the latest trends and best practices in machine learning.
  • You are willing to relocate to Los Angeles

Nice to Haves:

  • Experience in the aerospace, aviation, or transportation industry.
  • Knowledge of IoT and data integration in machine learning models.
  • Familiarity with cloud services (e.g., AWS, Azure, GCP).
  • Previous startup experience.