SkyGrid, a Boeing Company, exists to open the sky for autonomous flight. Based in Austin, Texas, SkyGrid builds ground based high-assurance third-party services to enable the safe operations and airspace integration of highly automated and autonomous aircraft. SkyGrid also acts as a key operational nexus for advanced air mobility, integrating and managing data, infrastructure, access, and traffic to support seamless and efficient air traffic services.
SkyGrid is seeking an Autonomy Engineer II to help develop cutting-edge solutions for airspace automation. This role will focus on designing and implementing autonomy functions to enable safe, scalable Advanced Air Mobility (AAM) operations and enhance Air Traffic Management (ATM). This position will contribute to refining SkyGrid’s autonomy architecture, translating high-level technology concepts into working prototypes to support real-time decision-making, conflict detection and resolution (CD&R), collaborative traffic management, and adaptive mission planning in complex airspace environments. This is a hybrid position based in Austin, TX, reporting to the Principal Airspace Automation Manager.
Performance Objectives:
- Utilize product design requirements and operational use-cases to develop functional architecture and preliminary algorithm.
- Progress service to Technical Readiness Level (TRL) 2 (conceptual development) and TRL 3 (prototype).
- Conduct unit and integration testing (TRL 4/5) and document a minimum viable product version of the service.
- Enhance the service capability and work with software engineers to transition service to high fidelity production environments (TRL 6/7).
- Update concepts, use cases, and engineering artifacts based on technology maturation.
Responsibilities:
- Lead the end-to-end development of autonomous air traffic management functions—from concept to prototype.
- Collaborate with product and systems engineers to define functional and performance requirements for autonomy-based functions.
- Collaborate with software engineers to design and develop integration interfaces within the overarching product codebase.
- Specify and execute Software-in-the-Loop (SIL) testing to validate autonomy functions and generate relevant test result artifacts.
- Drive innovation by brainstorming and developing novel autonomy solutions, contributing to research and development efforts in autonomous airspace management.
- Foster collaboration by engaging with external organizations, research institutions, and industry partners, as well as supporting technical publications and conference contributions.
Qualifications:
- B.S. in Computer Science, Aerospace Engineering, Robotics, or related field.
- Minimum of 3 years of autonomy engineering experience, with a strong focus on concept-to-prototype development.
- Work experience in algorithm development or R&D for autonomous systems.
- Programming Languages: Python, C/C++.
- Familiarity with R&D of algorithms and functions that enable autonomous systems.
- Development, integration, and testing in SIL simulation environment.
- Experience in air traffic management research and development.
- Experience with mathematical formulation of vehicle kinematics/dynamics, route planning, optimization, conflict detection and resolution algorithms
- Translation of advanced algorithms and mathematical concepts into clean, working code.
- Familiarity with AI/ML, robotics, or autonomous systems
Highly motivated, solution-oriented individual with a strong analytical mindset, who can:
- define the problem statement
- conduct state of the art analysis
- analyze problems that are not clearly defined
- identify possible solutions
- adapt solution scope to fit timelines
Preferred Qualifications:
- M.S. or Ph.D. in Computer Science, Aerospace Engineering, Robotics, or related field.
- Familiarity with aviation and air traffic management operations.
- Experience in R&D and functional development in one or more of the following areas:
- Transportation networks
- Tactical or strategic conflict detection and resolution for air vehicles
- Flight Planning
- Machine Learning
- Resource optimization
- Advanced Air Mobility
- Bayesian or probabilistic modelling
- Large Language Models
