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About This Role
Date Posted:
2026-10-18Country:
United States of America Location:
US-CT-REMOTE Position Role Type:
Remote U.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 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.
Pratt & Whitney is a world leader in the design, manufacture and service of aircraft engines and auxiliary power systems and has been revolutionizing modern flight for over 100 years.
Join us and help shape the future of aerospace and defense.
Our Pratt & Whitney Digital Technology team has an exciting new remote job opportunity for a Full Stack AI/ML Developer - Digital Products.
What You Will Do:
We have an exciting opportunity for a Full Stack | AI/ML Developer!
The successful candidate will be collaborative, technically strong, and passionate about building digital products that deliver impactful user experiences.
You will work across engineering, data science, AI/ML, design, and product teams to solve complex real-world problems and push the boundaries of what’s possible.
This role will have a hands-on impact on defining product vision, shaping roadmaps, integrating AI/ML capabilities, and delivering modern digital solutions that meet customer and business needs.
Key Responsibilities:
- Collaborate with cross-functional agile teams to design, build, and manage digital products from concept through launch and adoption.
- Develop front-end architectures and intuitive, responsive user interfaces.
- Leverage Palantir Foundry for data integration, ontology management, pipeline development, operational workflows, and deployment of data-driven applications and AI/ML use cases.
- Design and implement backend services using languages such as C#, Java, and Python.
- Build and maintain scalable APIs, microservices, and integration frameworks.
- Partner with Data Scientists and AI Engineers to integrate AI/ML models into applications, including inference pipelines, intelligent features, and model-driven user experiences.
- Build and maintain data and application pipelines that support AI/ML functionality and model deployment.
- Collaborate on feature engineering, model testing, validation, and operationalization to ensure stable and scalable AI usage.
- Apply DevOps practices across CI/CD, automated testing, cloud deployment, and containerized workloads.
- Influence stakeholders and support decision-making through clear communication, structured artifacts, and product storytelling.
- Participate in Agile workflows including sprint definition, backlog prioritization, and iterative product releases.
- Stay current on emerging technologies including cloud platforms, AI/ML frameworks, front-end and backend tools, and modern development practices.
- Ensure solutions meet both technical requirements and customer needs.
- Support UI/UX design standards and best practices.
Qualifications You Must Have:
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field with 12+ years of applicable work experience; OR an Advanced Degree with 10+ years of applicable work experience.
- 10+ years of experience in data engineering, data architecture, analytics, AI/ML, or related technical domains.
- 5+ years architecting cloud-native data platforms (Azure, AWS, or GCP).
- Deep experience with Databricks, Lakehouse architectures, and modern ELT/ETL tooling.
- Strong understanding of distributed systems, real‑time data streaming, data governance, and API/Integration architectures.
- Hands-on experience working with Palantir Foundry, including data pipelines transformations, ontology management, and application deployment.
- Demonstrated ability to design and implement enterprise‑scale data architecture solutions.
- Familiarity with AI frameworks and tooling such as MLflow, PyTorch, TensorFlow, and cloud‑native ML platforms (Azure ML, SageMaker, Vertex).
Qualifications We Prefer:
- 12+ years of experience in digital technology and/or product development.
- Experience with Azure, AWS, GCP, or other cloud platforms.
- Strong professional experience with Java and Spring Boot.
- Experience developing microservices and REST APIs.
- Proficient with CI/CD practices and production deployment.
- Experience with Agile software delivery.
- Experience with SDLC management.
- Experience with Docker, Kubernetes, and container orchestration platforms.
- Familiarity with web application security concepts.
- Experience with Apache Kafka and Kafka Streams.
- Experience building or supporting AI/ML features, including model integration, inference APIs, or application‑level ML workflows.
- Ability to explain technical concepts to engineering and non‑technical stakeholders.
- Strong interpersonal skills and ability to collaborate across teams.
- Strong problem‑solving skills and attention to detail.
- Ability to thrive in a fast‑paced environment with autonomy and accountability.
- Proven ability to drive modernization, cloud migration, or digital transformation initiatives. Learn More & Apply Now:
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.
Candidates will learn more about role type and current site status throughout the recruiting process. For onsite and hybrid roles, commuting to and from the assigned site is the employee’s personal responsibility.
- This requisition is eligible for an employee referral award. ALL eligibility requirements must be met to receive the referral award.
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: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pratt & Whitney, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($228K) sits 8% above the category median. Disclosed range: $157K to $298K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Pratt & Whitney AI Hiring
Pratt & Whitney has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hartford, CT, US. Compensation range: $298K - $298K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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