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About This Role
Company: Alaska Airlines The Team:
Guided by our purpose, core values, and leadership principles, we are creating an airline people love. Our corporate teams set the strategies and operational plans to ensure the success of our company. Whether we use our expertise in accounting, human resources, finance, planning, legal, marketing, or any of our operational divisions, our shared passion for travel and our guests is what motivates us to achieve excellence each day. If you share our passion for creating an airline people love, we want to hear from you.
Role Summary:
The Principal Artificial Intelligence (AI) Software Engineer role is the sole subject matter expert in production\-grade AI systems, platform architecture, and applied AI governance. (process/discipline in the company). As an individual contributor, this role defines long\-term strategy for AI adoption, technical standards, and cross\-functional integration (process/discipline) and exercises considerable latitude and initiative to solve highly ambiguous, high\-impact challenges that advance Alaska Airlines’ operational excellence, commercial differentiation, and AI maturity (complex problems).
Key Duties:
- Architect and lead end\-to\-end development and deployment of AI solutions leveraging LLMs, generative AI, and agentic technologies, establishing reusable architectures and patterns that drive automation, operational efficiency, and strategic business outcomes.
- Design and build the AI platform that enables organizational self\-service, maintaining ownership of system performance, reliability, and continuous improvement of AI capabilities.
- Exercise considerable latitude and initiative to solve ambiguous, high\-impact challenges where problem definition, solution approach, and success criteria require significant discovery, including scaling AI from pilot to production, integrating AI into legacy operational systems, and defining governance approaches for novel AI use cases.
- Make decisions on technical architecture, sourcing strategies including build, buy, or partner approaches, and sequencing of AI initiatives based on business impact, technical feasibility, and organizational readiness, setting direction that shapes how AI is delivered across Alaska.
- Influence across company and several levels up to lead cross\-functional prioritization and adoption of AI initiatives by influencing stakeholders across IT, business, and governance, risk, and compliance teams to ensure secure, compliant integration into enterprise systems.
- Partner with Product Management and business leaders to integrate AI/GenAI solutions into product roadmaps, ensuring technical feasibility and business value alignment.
- Ensure production excellence by implementing monitoring, evaluation frameworks, and optimization strategies that enable AI systems to meet or exceed performance targets and deliver measurable business results.
- Develop technical talent and set engineering standards through mentorship, technical leadership, and knowledge sharing that elevates the capabilities of the AI Engineering Team.
- Drive innovation through AI research and experimentation including evaluation of emerging technologies, new data sources, and advanced methodologies, with presentation of findings to executive leadership.
Job\-Specific Experience, Education \& Skills:
Required* 7 years of experience in software development experience with demonstrated expertise in multiple programming languages (Python, Java, Golang, C\+\+, or similar).
- Bachelor’s degree with a focus in Computer Science or an additional two years of relevant training/experience in lieu of this degree.
- High school diploma or equivalent.
- Minimum age of 18\.
- Must be authorized to work in the U.S.
Preferred* 3\+ years of experience leading technical architecture and design decisions for large\-scale distributed systems, with demonstrated ability to evaluate emerging technologies and drive strategic platform investments through build vs buy decisions.
- 5\+ years of experience designing, delivering, and operating ML/AI systems in production, including at least 2 years with Generative AI (LLMs, multi\-modal models, agent frameworks, tool\-calling, workflow orchestration systems).
- Demonstrated track record of leading cross\-functional AI initiatives from concept through production adoption and scaling, including establishing monitoring, evaluation frameworks, and continuous improvement practices.
- Master's degree or PhD in Computer Science or related technical field.
- Experience designing ML/AI platforms that serve organization wide product and engineering teams.
- Published research, patents, technical blog posts, or conference presentations on AI/ML systems and architectures.
- Experience implementing AI governance frameworks including security controls, risk assessment, and adherence to industry standards.
- Track record of evaluating build vs buy decisions and successfully adopting emerging AI technologies (e.g., transformer\-based models, agentic tool\-use patterns, embedded inference) with measurable business impact.
- Experience upskilling engineering and product teams from traditional software development to AI native workflows.
- Experience developing accessible technologies.
Job\-Specific Leadership Expectations:
Embody our values to own safety, do the right thing, be caring and kind, and deliver performance.
Salary Range: $141,250\-$211,900 / year Salary Details:
Pay will be based on multiple factors, including and not limited to location, relevant experience/level and skillset while balancing internal equity relative to other Alaska/Hawaiian/Horizon employees. Alaska/Hawaiian/Horizon is committed to fair, unbiased compensation along with competitive benefits in all locations in which we operate. Note: We don’t typically hire at the top of the range.
Total Rewards:
*Alaska Airlines, Hawaiian Airlines and Horizon Air pay and benefits can vary by company, location, number of regularly scheduled hours worked, length of employment, and employment status.*
- Free stand\-by travel privileges on Alaska Airlines, Hawaiian Airlines \& Horizon Air
- Comprehensive well\-being programs including medical, dental and vision benefits
- Generous 401k match program
- Annual bonus plans
- Generous holiday and paid time off
For more information about Alaska/Hawaiian/Horizon Total Rewards please visit our career site and view benefits.
Regulatory Information:
Equal Employment Opportunity Policy Statement
It is the policy of Alaska Airlines, Hawaiian Airlines and Horizon Air to comply with all applicable federal, state and local laws governing nondiscrimination in employment and to ensure equal opportunity in all terms, conditions, and benefits of employment or potential employment.
We also prohibit discrimination and harassment against any employee or applicant for employment because of race, color, religion, sex, national origin, age, disability, veteran status, genetic information and other legally protected categories.
We have established an EEO Compliance Program under Section 503 of the Rehabilitation Act of 1973 (“Section 503”) and the Vietnam Era Veteran’s Readjustment Assistance Act of 1974 (“VEVRAA”). All applicants and employees are treated without regard to their race, color, religion, sex, national origin, disability or protected veteran status. In addition, we have established an audit and reporting system to allow for effective measurement of its equal employment opportunity activities.
To implement this policy, we will:
(1\) Recruit, hire, train and promote qualified persons in all job titles, without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information and any other legally protected categories;
(2\) Ensure that employment decisions are based only on valid job requirements; and
(3\) Ensure that all personnel actions and employment activities such as compensation, benefits, promotions, layoffs, return from layoff, Alaska Airlines, Hawaiian Airlines and Horizon Air sponsored programs, and tuition assistance will be administered without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information and other legally protected categories.
Employees and applicants for employment will not be subjected to harassment, intimidation, threats, coercion or discrimination because they have engaged or may engage in (1\) filing a complaint, (2\) opposing any act or practice made unlawful by, or exercising any other right protected by, any Federal, State or local law requiring equal opportunity, including Section 503 and the equal opportunity provisions of VEVRAA, or (3\) assisting or participating in any investigation, compliance evaluation, hearing, or any other activity related to the administration of any Federal, State or local law requiring equal opportunity, including Section 503 and the equal opportunity provisions of VEVRAA. Government Contractor \& Department of Transportation (DOT) Regulations
Alaska Airlines, Hawaiian Airlines \& Horizon Air are regulated by the Department of Transportation (DOT – regulations, 49 CFR part 40\) and all applicants are advised that post\-offer and/or pre\-employment drug testing will be conducted to determine the presence of marijuana, cocaine, opioids, phencyclidine (PCP) and amphetamines or a metabolite of these drugs prior to any offer or employment or transfer into a safety\-sensitive position. Failure to submit to testing or positive indications of drug use will render the applicant ineligible for employment with Alaska Airlines/Hawaiian Airlines/Horizon Air and any employment offer will be withdrawn.
FLSA Status: Exempt Employment Type: Full\-Time Regular/Temporary: Regular Requisition Type: Management Location: Seattle \- Hub Featured Job: 0 L:: \#LI\-B
Salary Context
This $141K-$211K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Alaska Airlines, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $141K to $211K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Alaska Airlines AI Hiring
Alaska Airlines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in SeaTac, WA, US. Compensation range: $211K - $211K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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