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About This Role
Date Posted:
2026\-07\-13Country:
United States of America
Location:
US\-TX\-MCKINNEY\-513WA \~ 2501 W University Dr \~ WING A BLDG
Position Role Type:
Onsite
U.S. Citizen, U.S. Person, or Immigration Status Requirements:
The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance
Security Clearance Type:
DoD Clearance: TS/SCI
Security Clearance Status:
Active and existing security clearance required after day 1
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.
Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today’s mission and stay ahead of tomorrow’s threat. We deliver solutions that help our nation and allies defend freedom and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense.
Raytheon Software Engineering is seeking an experienced and delivery\-focused Associate Director– Artificial Intelligence (AI) Application Engineer to drive applied AI innovation across classified and unclassified environments. This individual will directly contribute to hands\-on development, organizational enablement, secure deployment of AI/Machine Learning (ML) capabilities, and advanced research transition. The ideal candidate is not a theorist but a proven executor who delivers real capability into programs and generates business value.
What You Will Do
- Integrate and deploy AI/ML models, especially Large Language Models (LLMs) and emerging architectures
- Build and operate secure AI pipelines across cloud, tactical, and embedded environments
- Transition prototypes into production systems; support applied research maturation
- Provide organizational enablement through guidance, training, and best practices
Collaborate across engineering, research, program leadership, and customers
- Evaluate new technologies, ensuring compliance with security and government standards
- Contribute to proposals and technical communication
- Design, integrate, and govern agentic AI systems with guardrails and operational safety
- Apply agent\-based workflows that improve engineering and mission execution
Qualifications You Must Have
- Typically requires a STEM degree with a minimum of 12 years’ relevant experience
- Experience as an AI Practitioner applying AI technologies to solve challenges including SW Dev, SW Security, automation and operational workflows
- Experience with delivery of production AI systems
- Experience designing, training, and deploying Large Language Models (LLMs) and advanced machine‑learning models, including implementing secure ML Ops and DevSecOps practices
- Experience with Python and modern machine \-learning frameworks
- Experience deploying AI in cloud and constrained/embedded environments
Experience leading workstreams
- Ability to obtain and maintain a U.S. Government issued TS/SCI security clearance after day 1\. U.S. Citizenship is required as only US Citizens can acquire clearance
Qualifications We Prefer
- Experience with delivery of production AI systems
- Classified program experience
- Embedded AI optimization experience
- Integration of AI into legacy/multi\-domain systems
- Familiarity with government AI assurance and safety standards
- Active U.S. Government issued TS/SCI security clearance
What We Offer
- Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation
- Relocation Eligibility
Learn More \& Apply Now!
- Please consider the following role type definition as you apply for this role
- 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
This position requires security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: https://www.state.gov/m/ds/clearances/c10978\.htm
Locations : McKinney, TX Tucson, AZ Tewksbury, MA
- We Are RTX
*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 157,200 USD \- 298,800 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.*
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Salary Context
This $157K-$298K range is above the 75th percentile 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 Raytheon, 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. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $157K to $298K.
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.
Raytheon AI Hiring
Raytheon has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span McKinney, TX, US, Tucson, AZ, US. Compensation range: $204K - $298K.
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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