Interested in this AI/ML Engineer role at Boeing?
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Seattle, Washington; Dallas, Texas
Job ID JR2026510793 Category Data Science \& Analytics Role Type Hybrid Post Date Jun. 01, 2026
Job Description
At Boeing, we innovate and collaborate to make the world a better place. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.
The Boeing Company is looking for a highly experienced and detail\-oriented Senior Artificial Intelligence (AI) Solutions Architectto join the team in Seattle, WA or Dallas, TX.
The Senior Artificial Intelligence (AI) Solution Architect, Boeing Global Services (BGS) Supply Chain will lead the end\-to\-end technical architecture for AI\-enabled solutions across the BGS Supply Chain organization. This role is responsible for designing scalable, secure, and production\-ready Artificial Intelligence (AI) solutions that span the full data and technology ecosystem, including on\-premises and cloud environments.
This individual will work closely with business leaders, product managers, data engineers, data scientists, platform teams, and enterprise architecture to translate raw data and data products into AI capabilities such as machine learning, generative AI, Agentic AI, deep learning, decision intelligence, and advanced automation. The ideal candidate will bring deep technical expertise in enterprise architecture, data and AI platforms, and supply chain use cases, with the ability to design end\-to\-end solutions from data ingestion through modeling, ontology, orchestration, and front\-end visualization.
Position Responsibilities:
- Lead the architecture and design of end\-to\-end AI solutions supporting BGS Supply Chain business needs
- Define solution architectures that integrate on\-prem and cloud\-based data, analytics, and AI capabilities into scalable enterprise patterns
- Translate business requirements into technical designs for AI\-enabled supply chain solutions
- Architect the full solution lifecycle from raw data and governed data products through data modeling, ontology, AI model development, orchestration, and visualization
- Design solutions using ML, Generative AI, Agentic AI, deep learning, optimization, and other advanced AI techniques as appropriate to the use case
- Partner with supply chain business stakeholders to identify high\-value use cases and shape feasible technical solutions
- Collaborate with data engineers, data scientists, software engineers, platform architects, and product teams to ensure end\-to\-end solution integrity
- Define solution patterns for data ingestion, transformation, semantic modeling, ontology design, AI model integration, workflow orchestration, and user experience delivery
- Ensure solutions are scalable, maintainable, secure, and aligned to Boeing architecture, cyber, governance, and compliance standards
- Evaluate technical tradeoffs across cloud, hybrid, and on\-prem environments and recommend the best\-fit architecture for business needs
- Provide architectural guidance for AI product teams throughout design, build, testing, deployment, monitoring, and enhancement phases
- Support the creation of reusable frameworks, reference architectures, and patterns for supply chain AI solutions
- Work with front\-end and product teams to ensure insights and AI outputs are delivered through intuitive interfaces, dashboards, copilots, or decision\-support tools
- Help define ontology and semantic layer approaches that improve usability, searchability, interoperability, and AI consumption of supply chain data
- Participate in technical governance, design reviews, and architecture decision forums
- Support production readiness, performance tuning, observability, and operational support for deployed AI solutions
- Stay current on emerging AI technologies and recommend innovations that can improve supply chain performance and business value
Basic Qualifications (Requires Skill/Experience):
- 10\+ years of experience with Artificial Intelligence (AI) and Machine Learning (ML) technologies, including the ability to integrate AI\-driven insights into data architecture and analytics processes
- 10\+ years s of experience with Information Technology architecture including cloud and data architecture in a large\-scale, hybrid cloud and on\-prem environment
- 10\+ years of experience communicating with technical experts and explaining difficult technical concepts to non\-technical business users
- 10\+ years of experience working across organizations and interfacing with key stakeholders, including senior leaders
- 5\+ years of experience designing, developing \& optimizing AI/ML solutions, data science workflows, and analytics methodologies
- 5\+ years of experience with AI/ML and generative AI lifecycle concepts, including model development, evaluation, deployment, monitoring, change management, documentation, and data governance
- 5\+ years of experience designing, developing \& optimizing AI/ML solutions, data science workflows, and analytics methodologies
Preferred Qualifications (Desired Skills/Experience)
- Bachelor’s degree or higher in computer science, engineering, information systems, data science, or related field
- Experience architecting AI solutions for supply chain, logistics, inventory, planning, sourcing, or parts domains
- Experience with ML platforms, Machine Leaning Operations (MLOps) Large Language Model (LLM) orchestration, vector databases, retrieval\-augmented generation, and model lifecycle management
- Experience designing semantic layers, ontologies, knowledge graphs, or enterprise data models
- Experience with Agentic AI, workflow automation, or AI\-based decision support solutions
- Experience with data visualization tools, orchestration platforms, and front\-end delivery patterns
- Experience with enterprise data governance, cataloging, lineage, and security frameworks
- Experience with event\-driven architecture, API\-based integration, and scalable cloud\-native design
- Strong understanding of data architecture, APIs, integration patterns, and production engineering concepts
- Experience supporting industrial, aerospace, or similarly complex operational environments
Conflict of Interest:
Successful candidates for this job must satisfy the Company’s Conflict of Interest (COI) assessment process.
Drug Free Workplace:
Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria are met as outlined in our policies.
Pay \& Benefits:
At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities.
The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work.
The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.
Pay is based upon candidate experience and qualifications, as well as market and business considerations.
Summary pay range: $184,450 \- $249,550
Applications for this position will be accepted until Jun. 16, 2026
Export Control Requirements:
This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120\.62 is required. “U.S. Person” includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee.
Export Control Details:
US based job, US Person required
Relocation
This position offers relocation based on candidate eligibility.
Visa Sponsorship
Employer will not sponsor applicants for employment visa status.
Shift
This position is for 1st shift
Equal Opportunity Employer:
Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
Your Benefits
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No matter where you are in life, our benefits help prepare you for the present and the future.
- Generous company match to your 401(k).
- Industry\-leading tuition assistance program pays your institution directly.
- Fertility, adoption, and surrogacy benefits.
- Up to $10,000 gift match when you support your favorite nonprofit organizations.
These programs are subject to eligibility requirements and other conditions, which may differ for employees of certain subsidiaries or business units, or union\-represented employees depending on bargaining agreement terms. If this information conflicts with the program documents, the latter shall control. This material is informational only.
Salary Context
This $184K-$249K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Boeing, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($217K) sits 20% above the category median. Disclosed range: $184K to $249K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Boeing AI Hiring
Boeing has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, Berkeley, MO, US. Compensation range: $233K - $249K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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