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About This Role
Date Posted:
2026\-06\-18Country:
United States of America
Location:
US\-CT\-EAST HARTFORD\-RTRC L \~ 411 Silver Ln \~ RTRC L
Position Role Type:
Hybrid
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. Join us and help shape the future of aerospace and defense.
The Dynamics, Control, and Autonomy Team, part of the Intelligent \& Cyber\-Physical Systems Department at RTX Technology Research Center (RTRC) is looking for a highly motivated individual for the position of research engineer specialized in Learning for Dynamics and Control.
RTRC serves as the innovation hub for RTX. We conduct basic and applied research in a stimulating multi\-disciplinary environment where scientists, engineers, practitioners and subject matter experts collaborate and exchange experience. We transform that research into the solutions and products that help our businesses shape the future. We are:
- Empowering innovation among the company’s businesses.
- Solving customers’ critical problems.
- Developing breakthroughs for a safer, more connected world.
- Working with major universities and national laboratories on groundbreaking research.
The Dynamics, Controls, and Autonomy team supports dynamical system analysis and modeling, control system analysis and design, and autonomous systems research for all RTX business units, including both development of novel solutions for future products and solving the toughest problems with current products. In parallel, we are working with government customers on more broadly applicable technology.
What You Will Do
- Design and develop novel control solutions for aerospace and defense applications including, but not limited to, jet engines, missiles, autonomous vehicles and systems, avionics, aircraft power systems and air management, hypersonic vehicles, advanced manufacturing, and space systems;
- Work in a multidisciplinary setting, bringing system\-level perspective to new cutting\-edge technologies from multiple fields (autonomy, power systems, cyber security, mechanical systems, aerodynamics, thermal management)
- Lead and support externally and internally sponsored programs, write external and internal research proposals;
- Disseminate research results through reports, conference proceedings, and peer\-reviewed articles, and developing intellectual property.
What You Will Learn
- How to transition novel concepts from early technology stages to a state that impacts and influences our products, which in turn have global impact on society
- How to build relationships both within our company, and externally with industry, academia, and government agencies for long\-term impact
Qualifications You Must Have
- Ph.D. in Mathematics, Physics, Computer Science or Engineering.
- Strong fundamentals in control:
+ standard multivariable control and estimation techniques (e.g., LQR/LQG, Kalman filters, optimization\-based control, including Model Predictive Control), from formulating the problem to implementation in software
- Experience with machine learning for control, including
+ Reinforcement Learning (RL) for safety\-critical systems (e.g., model\-based RL, Sim2Real transfer learning, or safety guarantees using Control Barrier Functions)
+ Verification \& Validation of AI/ML control laws
+ Neural\-network representations of controllers and estimators (e.g., Physics\-Informed Neural Networks for MPC, or Neural Network based MPC)
- Control\-oriented modeling of physical systems, both from first principles and data\-driven (including learning\-based methods such as Physics\-Informed Neural Networks)
- Proficiency in MATLAB/Simulink, Python, Pytorch or TensorFlow
Qualifications We Prefer
- Master degree in Mathematics, Physics, Computer Science or Engineering with minimum 5 years of full\-time industrial experience .
- Novel approaches for safety including Control Barrier Functions (CBF)
- Hardware\-in\-the\-Loop validation and real\-time/embedded implementation of control laws
+ experience with Speedgoat, dSPACE, or LabView/NIDAQ
+ FPGA programming
+ C/C\+\+ programming
- Experience with multi\-agent collaborative autonomy, including
+ Multi\-agent autonomous behaviors
+ Decentralized mission planning and execution
- Hands\-on experience with implementation of autonomy algorithms in high\-fidelity simulations and/or hardware platforms:
+ PX4 or ArduPilot autopilots and software\-in\-the\-loop simulations
+ Robot Operating System (ROS, ROS2\) and Gazebo simulation
+ Open\-source planning and perception software packages
+ Commercial UAV and UGV platforms
- Experience with one or more of the following technical areas:
+ Neural and symbolic AI approaches for course of action development
+ Resilient contingency management for multi\-agent autonomous systems
+ Human\-robot teaming
- Application experience in one or more of the following:
+ Manufacturing and inspection operations
+ autonomous systems, including assurance for autonomy
+ safety and certification in aerospace
+ gas turbine engine modeling and control;
+ Guidance, Navigation, and Control (aircraft, spacecraft, or missiles)
+ hypersonic propulsion;
+ electric or hybrid\-electric propulsion for aircraft;
+ control co\-design
- Experience with Large Language Models and agentic control
- Experience with writing proposals for government\-funded research, record of grants
- a record of innovation as evidenced by patent applications, a track record of writing proposals for government funded research programs, and/or high\-quality journal and conference publications.
- Active Security Clearance
Additional Skills and Abilities
-----------------------------------
- Strong analytical, problem\-solving and interpersonal skills with track record of teamwork, adaptability, innovation and initiative
- Clear and effective communication with all levels of management, business development, researchers and customers
- Ability to focus on results in a fast\-paced, dynamic team environment
- Ability to work independently with limited direction and in multidisciplinary environment to accomplish project goals
- The preferred candidate will look at open\-ended tough problems as an opportunity to innovate and develop novel solutions
Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:
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.
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.
*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 86,800 USD \- 165,200 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 $86K-$165K range is in the lower quartile for Research Scientist roles in our dataset (median: $195K across 149 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 4,317 AI roles we're tracking, Research Scientist positions make up 4% of the market. At RTX, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 378 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($126K) sits 43% below the category median. Disclosed range: $86K to $165K.
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.
RTX AI Hiring
RTX has 2 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in East Hartford, CT, US. Compensation range: $165K - $251K.
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 Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
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).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
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.
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