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About This Role
Date Posted:
2026\-07\-27Country:
United States of America
Location:
US\-IA\-CEDAR RAPIDS\-124 \~ 400 Collins Rd NE \~ BLDG 124
Position Role Type:
Onsite
U.S. Citizen, U.S. Person, or Immigration Status Requirements:
Must be authorized to work in the U.S. without the company’s immigration sponsorship now or in the future. The company will not offer immigration sponsorship for this position. The company will not seek an export authorization for this role.
Security Clearance Type:
None/Not Required
Security Clearance Status:
Not Required
At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world\-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.
Collins Aerospace is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense.
The AI Sandbox team within Applied Research \& Technology (ART) is seeking an Artificial Intelligence (AI) Infrastructure Engineer to architect and lead the evolution of our AI infrastructure for AI and GenAI applied research. This role facilitates rapid prototyping that transitions seamlessly into turnkey development environments, manages on\-prem and cloud infrastructure, and supports developers working within RTX Digital Technology (DT) frameworks.
This role will be worked onsite in Cedar Rapids, IA. Relocation assistance is available.
What You Will Do:
- Lead the architecture, design, and evolution of scalable AI infrastructure including GPU servers, Kubernetes clusters and supporting platform services
- Define the technical vision and deliver standardized AI development platforms and engineering workflows that enable secure, repeatable, and scalable AI research
- Enable AI researchers and developers by delivering secure, self\-service development environments, container platforms, and engineering tooling that accelerate innovation and improve productivity
- Architect and oversee end\-to\-end AI platform capabilities, including containerized environments, source control, CI/CD pipelines, and developer tooling
- Manage DT\-aligned platform operations including accounts, access, backups, compliance, and server configuration.
- Implement and track required approvals, authorizations, and compliance activities.
- Support and maintain on\-prem and cloud\-based environments within RTX’s DT ecosystem.
- Evaluate and recommend emerging tools; support sandboxed pilots for AI infrastructure and MLOps/LLMOps capabilities.
- Collaborate with ART, DT, and AI/ML teams to align infrastructure with research needs.
- Travel up to 25%
Qualifications You Must Have:
- Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 10 years prior relevant experience or an Advanced Degree in a related field and minimum 7 years of experience.
- Experience administering AI or compute\-intensive infrastructure including GPU servers and Kubernetes clusters.
- Experience with Linux, container runtimes, orchestration, and platform configuration.
- Experience operating within enterprise IT frameworks requiring security, compliance, and access controls.
- Experience supporting developers or researchers in platform, DevOps, or infrastructure engineering roles.
Qualifications We Prefer:
- Kubernetes certifications or equivalent expertise.
- Experience architecting, designing and operating AI infrastructure on Azure, AWS, or GPC, including GPU provisioning, Kubernetes services, networking, storage, identity and access management, security, and cost optimization.
- Familiarity with AI/ML platforms, MLOps, or GenAI development workflows.
- Experience with RTX DT processes, tools, and compliance requirements.
What We Offer:
Some of our competitive benefits package includes:
- Medical, dental, and vision insurance
- Three weeks of vacation for newly hired employees
- Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option
- Tuition reimbursement program
- Student Loan Repayment Program
- Life insurance and disability coverage
- Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection
- Birth, adoption, parental leave benefits
- Ovia Health, fertility, and family planning
- Adoption Assistance
- Autism Benefit
- Employee Assistance Plan, including up to 10 free counseling sessions
- Healthy You Incentives, wellness rewards program
- Doctor on Demand, virtual doctor visits
- Bright Horizons, child and elder care services
- Teladoc Medical Experts, second opinion program
- And more!
Learn More \& Apply Now!
Collins Aerospace makes modern flight possible. Of course, that wouldn’t be possible without the capabilities and technologies of our organization, as well as our engineers – a highly skilled, accomplished network that spans more than 180 sites, 24 countries and 6 Strategic Business Units (SBUs).
Our industry\-leading experts are setting the standards for the aerospace industry and paving the way for the future. But as new challenges present themselves, we need fresh, creative and motivated minds to overcome these hurdles, help us break barriers and achieve new levels of innovation. Do you have what it takes to join a global organization that doesn’t shy away from big opportunities? If so, we invite you to join our ranks and create the next generation of aerospace technologies.
Together, we will nurture an engineering culture that values intellectual curiosity, risk takers and integrity. A place where we will challenge ourselves, our teams, and the status quo and where we will work to find a way – the right way – to achieve what others can only dream of.
- Please ensure the role type (defined below) is appropriate for your needs before applying to this role. This position is classified as:
Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products.
*As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in\-person at one of our office locations, regardless of whether the role is designated as on\-site, hybrid or remote.*
The salary range for this role is 132,400 USD \- 251,600 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.
Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short\-term disability, long\-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective\-bargaining agreement.
Hired applicants may be eligible for annual short\-term and/or long\-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective\-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance.
This role is a U.S.\-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.
RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.*RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.*
Privacy Policy and Terms:
Salary Context
This $132K-$251K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Collins Aerospace, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($192K) sits 11% below the category median. Disclosed range: $132K to $251K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Collins Aerospace AI Hiring
Collins Aerospace has 3 open AI roles right now. They're hiring across AI Architect, AI Software Engineer, AI/ML Engineer. Positions span Charlotte, NC, US, Cedar Rapids, IA, US. Compensation range: $204K - $298K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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