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Apply Now →About This Role
Date Posted:
2026-09-29Country:
United States of America Location:
US-NY-REMOTE Position Role Type:
Remote 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 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. Join us and help shape the future of aerospace and defense.
The following position is to join our RTX Enterprise Services team:
RTX’s Enterprise Services Data (ES-Data) group is seeking a Responsible AI Engineer to support RTX’s focus on Responsible AI policy implementation as part of our enterprise-wide AI Governance initiative. This individual will support the RTX AI Governance team within ES-Data and partner with colleagues across RTX businesses to build, own, and scale AI governance capabilities. The role will focus on configuration of AI governance platforms, automation and integration with enterprise systems, reporting and data quality, vendor coordination, adoption/enablement, and operationalization of policy controls.
The Enterprise AI Governance Responsible AI Engineer will play a key role in designing, configuring, and maintaining governance workflows, evidence capture, and integrations across our AI/ML ecosystem—ensuring repeatable, auditable, and scalable controls for responsible AI. This position offers the opportunity to work with leading AI governance platforms and tools such as One Trust, Archer, WatsonX while contributing to critical enterprise-wide initiatives.
What You Will Do
- Configure and maintain enterprise AI governance capabilities, standardizing workflows, controls, and evidence practices to ensure consistent execution across teams and business units.
- Design and maintain scalable integrations between AI governance capabilities and core enterprise systems. Establish standardized data exchange and event-driven patterns that automate assessments, approvals, evidence capture, and reporting. Continuously reduce manual effort and improve repeatability, reliability, and audit readiness
- Enable governance metrics, analytics, and automated reporting processes. Implement data quality and traceability practices to ensure accuracy, consistency, and audit readiness
- Translate Responsible AI, privacy, and security policies into operational controls embedded in processes and platform configurations. Maintain a central control library and supporting artifacts in partnership with compliance stakeholders.
- Develop enablement materials, training, and onboarding guides. Provide hands-on support and office hours to drive adherence to governance workflows and continuous improvement in execution
- Coordinate with platform vendors for configuration guidance, support, and capability enablement. Evaluate functionality against enterprise requirements to keep solutions vendor-neutral and outcomes-focused
Qualifications You Must Have
- Bachelor’s degree in STEM or a related field, and a minimum of 8 years of relevant experience or an advanced degree with a minimum of 5 years of relevant experience.
- Experience architecting integrations and orchestrating automations using standardized patterns that streamline governance processes.
- Experience with One Trust - designing and configuring governance workflows and evidence management in enterprise environments
- Experience with reporting, including metrics design, automated reporting processes, and data quality and traceability practices.
- Experience with Responsible AI and governance frameworks and regulatory trends
- Experience operating with minimal oversight, manage moderately sized projects or processes, and coach or delegate work to lower-level professionals.
- Experience communicating with and influencing cross-functional teams Legal, Compliance, Security, Data and AI, and Business stakeholders, including solving complex cross-departmental problems
Qualifications We Prefer
- Hands-on experience with Archer or WatsonX AI Governance platforms
- Certifications such as AIGP, CIPP or CIPM or Responsible AI training
- Ability to translate Responsible AI frameworks into operational controls, measurable metrics, and audit‑ready artifacts.
- MLOps literacy, including model lifecycle concepts, model monitoring, drift detection, lineage, and dataset governance
What We Offer
Whether you’re just starting out on your career journey or are an experienced professional, we offer a robust total rewards package with compensation; healthcare, wellness, retirement and work/life benefits; career development and recognition programs. Some of the benefits we offer include parental (including paternal) leave, flexible work schedules, achievement awards, educational assistance and child/adult backup care.
Learn More & Apply Now!
Work Location: Remote
Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:
Remote: Employees who are working in Remote roles will work primarily offsite (from home). If you live within a reasonable commute of an RTX site with other colleagues you interact with, your manager will discuss whether there is a degree of onsite presence associated with this role.
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 107,500 USD - 204,500 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 $107K-$204K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At RTX, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $107K to $204K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
RTX AI Hiring
RTX has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Albany, NY, US, Cedar Rapids, IA, Charlotte, NC. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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