Interested in this AI/ML Engineer role at Airbus?
Apply Now →Skills & Technologies
About This Role
Airbus is committed to providing reasonable accommodations as an Equal Opportunity Employer to applicants with disabilities. If you require assistance or an accommodation to complete your application, please contact us at [email protected]
- Notice: Know Your Rights: Workplace Discrimination is Illegal
- Notice: Pay Transparency Nondiscrimination (English)
- Aviso: Transparencia en el Pago No Discriminación (Spanish)
Job Description:
Airbus North America Information Management (IM) is looking for a Lead Data & Analytics / AI Developer to join our AI and Advanced Analytics team.
You will be part of a team developing cutting-edge AI, machine learning, and enterprise data architecture. As part of the AI and Advanced Analytics team, you will be involved in leading data engineering and Palantir Skywise integration aiming at optimizing operational decision-making, assembly line efficiency, and productivity across North American business divisions.
Meet the Team
Reporting directly to the Head of AI and Advanced Analytics, you will join a high-impact regional team that serves as the digital backbone for Airbus Americas. We bridge technical execution, data architecture, and practical AI application development. Our team collaborates directly with shop-floor managers, digital specialists, and executive leadership across commercial aviation and helicopter divisions to transform complex physical assembly, supply chain, and manufacturing challenges into scalable technical assets.
Your Working Environment
This role can be based in Mobile, Alabama; Grand Prairie, Texas; Herndon, Virginia or Atlanta, GA.
How We Care for You
- Financial Rewards: Competitive base salary, incentive compensation which may include profit sharing schemes, retirement savings plan, and the ability to participate in an Employee Stock Ownership Plan (“ESOP”).
- Work/Life Balance: Paid time off including personal time, holidays, and a generous paid parental leave program.
- Health & Welfare: Comprehensive insurance coverage including medical (traditional and high-deductible health plans), prescription, dental, vision, life, disability, Employee Assistance Plan (“EAP”), and other supplemental benefit coverages.
- Individual Development: Upskilling and development opportunities through our global Leadership University, including unlimited access to 10,000+ e-learning courses focusing on ways to develop your employability, certifications, career path, as well as the opportunity to participate in accelerated development programmes and both national and international mobility.
Your Challenges
1. Data & Skywise Architecture, Pipeline Engineering & SME Leadership* Skywise Engineering: Design, build, and maintain robust, scalable end-to-end data pipelines and analytical workflows within Palantir Skywise (utilizing Code Repository, Pipeline Builder, Ontology, Slate, AIP Assist, and Workshop) to transform complex aviation manufacturing and flight data into operational insights.
- Platform Administration & Enablement: Oversee administration, scalability, and user enablement for enterprise analytics platforms, including Alteryx and Tableau.
- SME & Technical Governance: Act as the principal technical authority for Data Engineering and Palantir Skywise across North America Digital; establish development best practices, enforce data governance/security, and continuously optimize data assets.
- Business Translation: Partner with operational leadership across regional worksites (e.g., Mobile, AL, and Grand Prairie, TX) to translate assembly, logistics, and supply chain challenges into technical data requirements and data products.
2. AI Solution Development & Hands-On Execution* AI Solution Engineering: Work hands-on to build and deploy advanced analytics and AI applications, integrating Generative AI, machine learning pipelines, and custom AI personas into operational workflows.
- Emerging AI Adoption: Pioneer internal adoption of emerging AI developer tooling (e.g., AIP Assist, Gemini, domain-tailored AI agents) to automate coding, documentation, and data cleansing.
- Data Foundation for AI: Ingest, clean, catalog, and structure disparate data sources (APIs, databases, streaming feeds, flat files) into robust feature sets and Ontologies.
- System Optimization & MLOps: Monitor, evaluate, and optimize existing analytical and AI applications to maintain low latency, reduce technical debt, and ensure continuous business value.
3. Technical Coaching, Upskilling & CoE Support* Upskilling & Workshops: Design, lead, and facilitate technical workshops and enablement sessions to expand analytics and AI adoption across regional worksites.
- Technical Guidance: Mentor junior data analysts, engineers, and citizen developers, conducting peer code reviews to elevate technical excellence.
- CoE Alignment: Partner with the North American AI Center of Excellence (CoE) to build capability, establish execution templates, and foster a data-driven, AI-first culture across Airbus Americas.
Your Boarding Pass
Experience & Education* Required Experience: Minimum of 10 years of professional experience in data engineering, advanced analytics, or business intelligence (or 7+ years with a Master's degree).
- Education: Bachelor’s degree in Computer Science, Data Science, Business Analytics, Engineering, Information Systems, or a quantitative field (Master’s degree preferred).
- Domain Track Record: Demonstrated experience leading digital initiatives and pipeline delivery within aerospace, manufacturing, transportation, or complex operational environments.
- Leadership: Proven track record acting as a lead technical developer/SME, mentoring junior developers, and establishing code governance in agile teams.
Technical Capabilities & Skills* Palantir Ecosystem: 3+ years of deep, hands-on experience in Palantir Skywise (Pipeline Builder, Workshop, Slate, AIP Assist, Code Repository, Ontology, Contour, Code Workbook).
- Core Languages: Advanced technical proficiency in Python, PySpark, SQL, and Spark SQL to build performant, fault-tolerant pipelines.
- AI Engineering: Proven hands-on experience with GenAI models, prompt engineering, agentic workflows, and deploying AI/ML models into production workflows.
- Enterprise BI: Expert knowledge of Tableau, Alteryx, and PowerBI.
- Communication: Exceptional ability to translate complex data architectures and AI algorithms into actionable, plain-language business insights for non-technical leadership.
- Languages: Fluent written and spoken English.
Desirable / Preferred Qualifications* Prior experience in an international corporate environment with transatlantic cross-border coordination.
- Experience with cloud platforms (GCP, AWS, Azure), containerization (Docker, Kubernetes), and MLOps pipelines.
- Palantir Foundry / Skywise Certifications.
- SAFe Agile Certification or PMP credentials.
Cloud or Data Engineering certifications (Databricks, AWS, Azure, GCP). *
Nature of Contacts:
- Daily communication with Digital domain managers, Product Owners, data engineers, and AI delivery teams.
- Daily interaction with internal and external stakeholders across multiple functional departments (shop floor, logistics, manufacturing) and executive leadership.
- Regular collaboration with business unit representatives across Commercial Aviation (A320 FAL) and Airbus Helicopters.
Periodic alignment and technical coordination with global European IM and Skywise core platform teams. *
Physical Requirements:
- Onsite: 60%
- Vision: Daily able to see and read computer screens and other electronic equipment with screens, able to read documents, reports and engineering drawings.
- Hearing: Daily able to participate in conversations in person and via teleconference or phone and to hear sounds on the production floor including safety warnings or alarms.
- Speaking: Daily able to speak in conversations and meetings, deliver information and participate in communications.
- Equipment Operation: daily use of personal computer, telephone, copies, fax machine, and related office equipment and using electronic identification card to enter building floors.
- Carrying: Daily able to carry documents, tools, drawings, electronic equipment up to 10lbs.
- Sitting: Daily able to sit for long periods of time in meetings, working on the computer.
- Standing: Daily able to stand for discussions in offices or on the production floor.
- Travel: 10% of time able to travel independently and at short notice.
- Walking : Daily able to walk through office and production areas including uneven surfaces.
Personal Protective Equipment required: Required PPE includes, but is not limited to, Safety Shoes, Safety Glasses, Hearing Protection, Respirators/Masks, and/or Protective Gloves as required by site and/or customer site *
Equal Opportunity:
All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, age, marital status, pregnancy, genetic information, or other legally protected status
As a leader in our field, Airbus provides relocation assistance for qualified positions and a comprehensive compensation and benefits package.
As a matter of policy, Airbus does not sponsor visas for US positions unless specified. Only applicants with current work authorization will be considered.
Airbus does not offer tenured or guaranteed employment. Employment with Airbus is at will, meaning either the company or the employee can terminate the employment relationship at any time, with or without cause, with or without notice.
NOTE: Airbus reserves the right to revise or change job duties and responsibilities as the need arises. This position description does not constitute a written or implied contract of employment.
This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Company:
Airbus Americas, Inc. Employment Type:
US - Direct Hire Experience Level:
Professional Remote Type:
On-site Job Family:
Digital <JF-IM-DI> -
-
Airbus provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetics, pregnancy, marital status, veteran status or other legally protected status. In addition to federal law requirements, Airbus complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, demotion, termination, layoff, recall, transfer, leaves of absence, compensation, benefits and training. Airbus expressly prohibits any form of workplace harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetics, pregnancy, marital status, veteran status or other legally protected status. As a matter of policy, Airbus does not sponsor visas for US positions unless specified. Only applicants with current work authorization will be considered. Airbus does not offer tenured or guaranteed employment. Employment with Airbus is at will, meaning either the company or the employee can terminate the employment relationship at any time, with or without cause, with or without notice. Airbus reserves the right to revise or change job duties and responsibilities as the need arises. This position description does not constitute a written or implied contract of employment. By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.
Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.
Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to [email protected] .
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Airbus, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Airbus AI Hiring
Airbus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.