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About This Role
Date Posted:
2026\-07\-21Country:
United States of America
Location:
US\-PR\-AGUADILLA\-110 \~ Rd 110 N Km 28\.8 \~ RD110
Position Role Type:
HybridU.S. Citizen, U.S. Person, or Immigration Status Requirements:
U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.Security Clearance:
None/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.
We are seeking a motivated Data Analytics Engineer with foundational knowledge in Mechanical Engineering and Machine Learning. In this role, you will analyze aircraft services and engineering datasets, uncover performance insights, and develop predictive models that support forecasting, field performance and reliability initiatives.
A solid understanding of mechanical engineering fundamentals will help you interpret technical data and collaborate effectively with subject matter experts. This analytics‑focused role works closely with engineering discipline owners, offering opportunities for professional growth and meaningful contributions to data driven reliability improvements.
This position will sit at our Aguadilla, PR location. You must be residing in Puerto Rico at the time of starting employment. Relocation is not offered.
This role is categorized as hybrid, with 3 days onsite and 2 days remote following the schedule assigned by the Manager.
What YOU will do:
- Analyze engineering and aircraft performance datasets to generate actionable insights for product performance and reliability.
- Perform data curation, cleaning, integration, and preparation for analytics, modeling, and machine learning workflows.
- Work with strain gauge, structural, thermal, and flight/aircraft data to extract features, identify trends, and detect anomalies or predictive indicators.
- Develop, train, and validate machine learning (ML) models (e.g., supervised learning, unsupervised learning, clustering, anomaly detection) for applications such as predictive maintenance, service interval estimation, and performance forecasting.
- Define validation methods, acceptance criteria, and performance metrics for analytical and predictive models.
- Generate data\-driven insights and recommendations for design, reliability, service engineering, and customer support teams.
- Conduct statistical analyses to evaluate the quality and completeness of engineering or enterprise data.
- Collaborate with engineering teams to support investigations and continuous improvement initiatives using enterprise data resources.
- Prepare technical documentation, presentations, and reports for both technical and non\-technical audiences.
- Occasionally travel domestically and/or internationally to support project requirements.
What YOU will learn:
- You will learn about our growing engineering team in Puerto Rico; What we do? Who we support? How we work?
- You will learn the technologies of today and tomorrow which we count on to maintain world leadership in the aerospace industry.
- You will learn why people enjoy and feel fulfilled by working in our industry.
Qualifications you must have:
- Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and 2 years prior relevant experience or an Advanced Degree in a related field.
- Demonstrated professional experience communicating in English (verbal and written).
- U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.
- Experience in data analytics workflows, statistical methods, and engineering data interpretation (internship/co\-op experience qualifies)
- Hands\-on experience with Python for data analysis (e.g., NumPy, Pandas, PySpark).
- Practical knowledge of machine learning concepts, with experience developing and validating models for prediction, classification, or anomaly detection.
- Foundational understanding of mechanical engineering principles (e.g., structural behavior, dynamics, thermal concepts) or familiarity with aircraft systems.
- Strong analytical and problem‑solving abilities, with the capability to communicate technical insights clearly to engineering and non‑technical stakeholders.
- Proficiency with Microsoft Office tools.
Qualifications We Prefer:
- Degree in Mechanical Engineering, Aerospace Engineering, or a closely related STEM field
- Experience in aerospace, defense, or related industry.
- Familiarity with database tools and SQL for data extraction and manipulation.
- Exposure to data visualization tools (e.g., Tableau, Power BI).
- Experience with Agile methodologies and tools such as JIRA and Confluence.
- Exposure to digital thread concepts, PLM systems, or model\-based engineering workflows.
- Experience applying machine learning (ML) models to mechanical engineering or physical‑system datasets.
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
- Participation in the Employee Scholar Program (ESP)
- Life insurance and disability coverage
- Employee Assistance Plan, including up to 8 free counseling sessions.
- And more!
Learn More \& Apply Now!
Collins Aerospace, an RTX business, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers’ toughest challenges and to meet the demands of a rapidly evolving global market.
Join our growing engineering team in Puerto Rico, where you will provide critical support to all Collins SBUs, working on exciting programs and projects ranging from the development of the next generation of advanced concept ejection seats to the latest technologies for the U.S. warfighter.
WE ARE REDEFINING AEROSPACE.
- Please consider the following role type definition as you apply for this role.
Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.
At Collins, the paths we pave together lead to limitless possibility. And the bonds we form – with our customers and with each other \- propel us all higher, again and again.
Apply now and be part of the team that’s redefining aerospace, every day.
*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.*
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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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Collins Aerospace AI Hiring
Collins Aerospace has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Aguadilla, PR, US, Cedar Rapids, IA, US. Compensation range: $131K - $204K.
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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