Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques * Knowledge of mission data ...
New
Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques * Knowledge of mission data ...
New
Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques * Knowledge of mission data ...
Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques * Knowledge of mission data ...
Principal Agentic AI Engineer (T5)
Indianapolis, IN · On-site
$180 - $240/hr
Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro‑symbolic techniques * Knowledge of mission ...
Posted today
Principal Agentic AI Engineer (T5)
Indianapolis, IN · On-site
$180 - $240/hr
Background in autonomous systems engineering, robotics, or intelligent control * Familiarity with reinforcement learning, planning algorithms, or neuro‑symbolic techniques * Knowledge of mission ...
Posted today
Advisor - Agent Research
Indianapolis, IN · On-site
... and robotics to make scientific discovery intelligent, autonomous, and fast. We're seeking a ... Design and build reinforcement learning (RL) environments that wrap real discovery tasks with ...
Advisor - Agent Research
Indianapolis, IN · On-site
... and robotics to make scientific discovery intelligent, autonomous, and fast. We're seeking a ... Design and build reinforcement learning (RL) environments that wrap real discovery tasks with ...
Reinforcement Learning Robotics information
What is reinforcement learning in robotics?
Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.
What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?
One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.
What are the key skills and qualifications needed to thrive as a reinforcement learning robotics engineer, and why are they important?
To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.
What is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?
| Aspect | Reinforcement Learning Robotics | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Degree in Robotics, Computer Science, or related fields; knowledge of reinforcement learning | Degree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms |
| Work Environment | Robotics labs, manufacturing, autonomous systems | Tech companies, data-driven projects, software development |
| Industry Usage | Autonomous robots, industrial automation, research | Data analysis, predictive modeling, AI applications |
Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.
Full-time
Medical, Life, Retirement, PTO
Posted 3 days ago
New
V2X rating
9.0
Based on 43 frontline employees who took The Breakroom Quiz
52nd of 450 rated engineering
Job description
Overview
Responsibilities
Qualifications
Working across the globe, V2X builds smart solutions designed to integrate physical and digital infrastructure from base to battlefield. We bring 120 years of successful mission support to improve security, streamline logistics, and enhance readiness. Aligned around a shared purpose, our $3.9B company and 16,000 people work alongside our clients, here and abroad, to tackle their most complex challenges with integrity, respect, responsibility, and professionalism.
Responsibilities
V2X is seeking an accomplished Principal Agentic AI Engineer (T05) to join our Software Department in Indianapolis, Indiana.
The ideal candidate embraces modern AIdriven development processes and engineering techniques that accelerate capability delivery, improve mission effectiveness, and enhance team productivity. They have experience designing, building, and integrating advanced agentic systems, including autonomous decision-support frameworks, intelligent mission planning tools, adaptive control systems, multisensor fusion pipelines, and AI-enabled situational awareness technologies. We are looking for engineers who enjoy tackling complex, emerging challenges and have a passion for supporting our DoD customers and international partners.
The Principal Agentic AI Engineer provides technical leadership across the full system development lifecycle, from conceptual architecture and requirements definition through model development, agentic workflow design, simulation, verification, validation, and final deployment. The role requires strong communication skills, collaborative problemsolving, and the ability to translate complex mission needs into practical, advanced AI solutions.
Qualifications
Education Requirements:
- Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science, or a related STEM field.
Required Skills:
- Must be a U.S. Citizen with the ability to obtain a DoD Secret Clearance
- At least 12 years of related engineering experience in systems, software, or hardware engineering.
- Deep expertise in designing and implementing agentic AI architectures and autonomous decision-making systems
- Strong proficiency in modern machine learning techniques, including model development, evaluation, and optimization
- Experience building reasoning, planning, and multi-step workflow automation using AI agents
- Ability to design and evaluate autonomous behaviors, control logic, and adaptive system responses
- Skilled in developing scalable AI pipelines, simulation frameworks, and experimentation environments
- Strong understanding of safety, reliability, and assurance considerations for AI-driven systems
- Proficiency with Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, JAX, LangChain-style orchestration)
- Ability to translate mission objectives and operational constraints into technical system requirements
- Experience with data engineering practices including preprocessing, feature extraction, and sensor/mission data handling
- Strong analytical problem-solving skills and ability to lead complex technical efforts
- Excellent communication skills for describing AI system behavior, trade-offs, and technical decisions to diverse stakeholders
- Familiarity with distributed systems, cloud platforms, or edge compute environments for model deployment
- Ability to guide junior engineers, provide technical mentorship, and set engineering direction
Desired Skills:
- Experience developing multi-agent systems or orchestrating large-scale agentic workflows
- Background in autonomous systems engineering, robotics, or intelligent control
- Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques
- Knowledge of mission data pipelines, sensor fusion, or realtime decision-support systems
- Experience with high-assurance or safety-critical AI development practices
Proficiency in designing AI evaluation frameworks, synthetic data generation, or simulation-based testing - Understanding of humanmachine teaming concepts and explainable AI practices
- Experience deploying AI models on edge compute platforms or constrained environments
- Familiarity with model governance, ML Ops, or lifecycle management tools
- Prior DoD, aerospace, or defense industry experience supporting mission-driven AI solutions
- Ability to contribute to technical roadmaps, architecture definitions, and long-term AI strategy
Security Clearance and Other Requirements
Ability to access systems with information restricted to U.S. citizens as required by federal contract guidelines.
Eligibility to obtain and maintain a U.S. Department of Defense (DoD) security clearance may be required for certain projects.
#LI-CB2
#clearance
Benefits include the following:
- Healthcare coverage
- Retirement plan
- Life insurance, AD&D, and disability benefits
- Wellness programs
- Paid time off, including holidays
- Learning and Development resources
- Employee assistance resources
Pay and benefits are subject to change at any time and may be modified at the discretion of the company, consistent with the terms of any applicable compensation or benefit plans.
At V2X, we are deeply committed to both equal employment opportunity, including protection for Veterans and individuals with disabilities, and fostering an inclusive and diverse workplace. We ensure all individuals are treated with fairness, respect, and dignity, recognizing the strength that comes from a workforce rich in diverse experiences, perspectives, and skills. This commitment, aligned with our core Vision and Values of Integrity, Respect, and Responsibility, allows us to leverage differences, encourage innovation, and expand our success in the global marketplace, ultimately enabling us to best serve our clients.
Qualifications:Education Requirements:
- Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science, or a related STEM field.
Required Skills:
- Must be a U.S. Citizen with the ability to obtain a DoD Secret Clearance
- At least 12 years of related engineering experience in systems, software, or hardware engineering.
- Deep expertise in designing and implementing agentic AI architectures and autonomous decision-making systems
- Strong proficiency in modern machine learning techniques, including model development, evaluation, and optimization
- Experience building reasoning, planning, and multi-step workflow automation using AI agents
- Ability to design and evaluate autonomous behaviors, control logic, and adaptive system responses
- Skilled in developing scalable AI pipelines, simulation frameworks, and experimentation environments
- Strong understanding of safety, reliability, and assurance considerations for AI-driven systems
- Proficiency with Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, JAX, LangChain-style orchestration)
- Ability to translate mission objectives and operational constraints into technical system requirements
- Experience with data engineering practices including preprocessing, feature extraction, and sensor/mission data handling
- Strong analytical problem-solving skills and ability to lead complex technical efforts
- Excellent communication skills for describing AI system behavior, trade-offs, and technical decisions to diverse stakeholders
- Familiarity with distributed systems, cloud platforms, or edge compute environments for model deployment
- Ability to guide junior engineers, provide technical mentorship, and set engineering direction
Desired Skills:
- Experience developing multi-agent systems or orchestrating large-scale agentic workflows
- Background in autonomous systems engineering, robotics, or intelligent control
- Familiarity with reinforcement learning, planning algorithms, or neuro-symbolic techniques
- Knowledge of mission data pipelines, sensor fusion, or realtime decision-support systems
- Experience with high-assurance or safety-critical AI development practices
Proficiency in designing AI evaluation frameworks, synthetic data generation, or simulation-based testing - Understanding of humanmachine teaming concepts and explainable AI practices
- Experience deploying AI models on edge compute platforms or constrained environments
- Familiarity with model governance, ML Ops, or lifecycle management tools
- Prior DoD, aerospace, or defense industry experience supporting mission-driven AI solutions
- Ability to contribute to technical roadmaps, architecture definitions, and long-term AI strategy
Security Clearance and Other Requirements
Ability to access systems with information restricted to U.S. citizens as required by federal contract guidelines.
Eligibility to obtain and maintain a U.S. Department of Defense (DoD) security clearance may be required for certain projects.
#LI-CB2
#clearance
Benefits include the following:
- Healthcare coverage
- Retirement plan
- Life insurance, AD&D, and disability benefits
- Wellness programs
- Paid time off, including holidays
- Learning and Development resources
- Employee assistance resources
Pay and benefits are subject to change at any time and may be modified at the discretion of the company, consistent with the terms of any applicable compensation or benefit plans.
At V2X, we are deeply committed to both equal employment opportunity, including protection for Veterans and individuals with disabilities, and fostering an inclusive and diverse workplace. We ensure all individuals are treated with fairness, respect, and dignity, recognizing the strength that comes from a workforce rich in diverse experiences, perspectives, and skills. This commitment, aligned with our core Vision and Values of Integrity, Respect, and Responsibility, allows us to leverage differences, encourage innovation, and expand our success in the global marketplace, ultimately enabling us to best serve our clients.
Education:UNAVAILABLEEmployment Type: FULL_TIME
About V2X
Sourced by ZipRecruiter
Industry
Guided missile and space vehicle manufacturing
Company size
10,000+ Employees
Headquarters location
McLean, VA, US
Year founded
1945