Director AI Data and Infrastructure

Founded in 2015, Acubed is the Silicon Valley innovation center of Airbus. As a global leader in aerospace, Airbus aims to make things fly. Our mission is to provide a lens into the future for the industry, transforming risk into opportunity to build the future of flight now.

At Acubed, we strive to propel innovation to market faster, broaden the talent pool in emerging aerospace careers and simultaneously help drive a culture change across Airbus.

WAYFINDER

Our Wayfinder team is building scalable, certifiable autonomy systems to power the next generation of commercial aircraft. Our team of experts is driving the maturation of machine learning and other core technologies for autonomous flight; we are creating a reference architecture that includes hardware, software, and a data-driven development process to allow aircraft to perceive and react to their environment. Autonomous flight is transforming the transportation industry, and our team is at the heart of this revolution.

The Opportunity/Role Description

As the Director of AI Data and Infrastructure, you will lead the design, implementation, and ongoing optimization of a scalable, data-driven development framework to aggregate, process, and manage data from diverse sources, including aircraft and simulation platforms. This framework will be critical in enabling worldwide data collection and ensuring seamless access for Airbus teams across the globe, supporting the development and testing of AI and autonomous flight functions.

Your responsibilities will include tailoring this solution to meet the unique needs of AI and autonomy flight function development, while also addressing the challenges of a globally distributed workforce in the US and Europe. In collaboration with global Airbus teams, you will strategize and oversee the expansion of this infrastructure, integrating existing technologies and solutions from other divisions where applicable to maximize efficiency and synergy.

This role is essential in establishing Airbus’s leadership in autonomous flight systems by delivering state-of-the-art, scalable, and reliable data and infrastructure solutions that empower cutting-edge innovation.

Responsibilities:

• Lead the Development of Scalable Data-Driven Systems: Drive the design, prototyping, and deployment of scalable, data-centric development environments across Airbus, with a primary focus on enabling and optimizing the development and testing of safety-critical autonomous flight solutions.
• Manage Data Labeling Quality: Oversee labeling techniques to ensure high accuracy in image data annotation. Develop and enforce guidelines for human-in-the-loop verification of labeled data to maintain quality standards.
• Build and Optimize Data Systems for Autonomy: Architect and implement robust systems to aggregate large volumes of function-specific data, prepare this data for development and testing, and support comprehensive verification and validation processes at scale for target autonomous functions.
• Ensure Uncompromising Quality Standards: Establish and enforce world-class QA processes and metrics to maintain the highest levels of data and system quality, ensuring reliability and safety in all aspects of autonomous flight function development.
• Expand Global Autonomous Function Solutions: Scale autonomous function solutions to create a globally integrated Airbus infrastructure that connects and synchronizes development efforts across Airbus facilities in the US and Europe.
• Engage with Stakeholders and Vendors: Act as the key interface between Airbus stakeholders, external vendors, and internal teams to ensure alignment and optimal resource utilization across AI data and infrastructure initiatives.
• Guide Architectural Decisions for AI and Autonomy: Provide strategic guidance on architecture, design, and implementation decisions, with a focus on advancing AI data systems and computing frameworks to support autonomous flight function development.
• Drive Cross-Functional Collaboration: Collaborate with diverse teams, including software engineering, tooling, flight test, simulation, data engineering, and machine learning, to remove bottlenecks in data availability, refine data collection methodologies, and ensure exhaustive and high-quality data coverage for development and testing.

Requirements:

Educational Background:

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related discipline.

Experience and Expertise:

• 10+ years of professional experience in large-scale data processing and MLOps infrastructure.
• 5+ years of experience managing high-performing teams in a dynamic, results-driven environment.
• Proficiency in implementing robust data quality assurance processes, including automated validation checks and manual verification workflows.
• Proven expertise in distributed data storage solutions, on-prem and cloud GPU/CPU clusters, and image data labeling techniques.
• Proficiency with tools and technologies for managing and improving large-scale data storage and processing.
• In-depth understanding of data pipelines, with demonstrated success in optimizing them for performance and cost-efficiency.
• Experience in machine learning-based image labeling techniques and methods, like pretrained models, bigger offline models and VLMs.

Soft Skills:

• Demonstrated ability to collaborate across both technical and non-technical teams, ensuring alignment and focusing on achieving measurable, data-driven results.
• Exceptional problem-solving skills and attention to detail, particularly in regulated, safety-critical environments.

Preferred qualifications:

• Extensive experience in large-scale data management, infrastructure, and processes specifically tailored to machine learning-based autonomous vehicle applications, including expertise with GPU-accelerated computing.

• Familiarity with active learning and embedding based image data clustering and  similarity search, data mining, to ensure good data distribution and coverage for safe and reliable autonomous flight functions
• Hands-on experience implementing ML-based and manual solutions for labeling, reviewing labels, categorization, and large-scale data processing, with a focus on scalability, performance, and automation.
• Working with both real and synthetics data sources, and support scaling of ingestion of both and scaling generation of synthetic data.

Compensation:

The estimated salary range for this position is $236,000 to $301,000 annually.  Enjoy comprehensive benefits: health insurance, paid time off, holidays, 401(k), Flexible Spending Account, Health Savings Account, Airbus Employee Share Ownership Plan, flight training, and more.

Experience flexibility with our hybrid work model, which includes three days in the office to foster collaboration and innovation while allowing for remote work options. Additionally, employees can work remotely—inside or outside the U.S.—for up to 31 days per year.

Why Join Us?

Be a part of a dynamic team that values creativity, collaboration, innovation and problem solving.  At Acubed, your contributions will directly impact our digital future.

Acubed is committed to creating a fair and equitable workplace for all. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, veteran status, marital status, pregnancy or parental status, or disability. Applicants will not be discriminated against based on these or other protected categories or social identities.

Acubed Requirements

All job offers at Acubed are contingent upon the candidate passing references, background and export control checks.

* Please Note that Acubed does not offer sponsorship of employment-based nonimmigrant visa petitions for this role.