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Reinforcement Learning Robotics Jobs in Texas (NOW HIRING)

Sincera Technologies, Inc.

AI/ML Engineer

Dallas, TX · On-site

Experience with robotics simulation, reinforcement learning, computer vision , or autonomous systems is highly preferred. * Familiarity with the NVIDIA GPU ecosystem and AI acceleration technologies.

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

  • Medical

  • Dental

  • Vision

  • PTO

... robotics and machine learning to enable safe self-driving at scale, with advanced techniques in imitation and reinforcement learning, planning and search, perception and prediction, simulation ...

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

... robotics and machine learning to enable safe self-driving at scale, with advanced techniques in imitation and reinforcement learning, planning and search, perception and prediction, simulation ...

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

  • Medical

  • Dental

  • Vision

  • PTO

... robotics and machine learning to enable safe self-driving at scale, with advanced techniques in imitation and reinforcement learning, planning and search, perception and prediction, simulation ...

BI Solutions Inc

WatsonX Assistant

Coppell, TX · On-site

... and reinforcement learning 2. Natural Language Processing (NLP) - Developing AI systems that ... Robotics Process Automation (RPA) - Designing AI-driven bots that execute repetitive tasks ...

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Reinforcement Learning Robotics information

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 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 is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData 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.

What are popular job titles related to Reinforcement Learning Robotics jobs in Texas? For Reinforcement Learning Robotics jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Robotics jobs in Texas look for? The top searched job categories for Reinforcement Learning Robotics jobs in Texas are:
What cities in Texas are hiring for Reinforcement Learning Robotics jobs? Cities in Texas with the most Reinforcement Learning Robotics job openings:
Reinforcement Learning Robotics jobs near you

Senior Software Engineer, Simulation & World Models

Bot Auto

Houston, TX • On-site, Remote

Full-time

Re-posted 7 days ago

Job description

About the Role

We are building the next generation of autonomous trucking technology to make freight transportation safer, more efficient, and more scalable.

Our Simulation team develops the virtual environments, testing infrastructure, and AI-driven simulation systems that enable rapid development and validation of autonomous driving software. We leverage large-scale simulation, synthetic data generation, reinforcement learning environments, and emerging world-model technologies to accelerate autonomy development.

We are seeking a software engineer with strong C++ expertise and a passion for building scalable simulation systems. This role offers the opportunity to work at the intersection of autonomous driving, simulation, robotics, and AI.

What You'll DoBuild Autonomous Driving Simulation Systems
  • Design and develop high-performance simulation infrastructure for autonomous vehicle development and validation
  • Build scalable systems for scenario generation, simulation execution, and evaluation
  • Develop simulation tooling used by Perception, Prediction, Planning, and Controls teams
  • Improve simulation realism, scalability, and operational efficiency
  • Collaborate across teams to support testing, validation, and development workflows
Develop AI-Driven Simulation Capabilities
  • Build infrastructure supporting reinforcement learning and closed-loop evaluation workflows
  • Develop systems for synthetic data generation and automated scenario creation
  • Collaborate with ML engineers and researchers to integrate learned models into simulation environments
  • Explore emerging approaches in world modeling, agent simulation, and Physical AI
Engineering Excellence
  • Write production-quality C++ and Python code
  • Participate in architecture and technical design discussions
  • Build reliable, maintainable, and well-tested systems
  • Contribute to code reviews and engineering best practices
  • Create clear technical documentation for systems and tools
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or a related field
  • 3+ years of professional software development experience
  • Strong expertise in modern C++ (C++17 or newer preferred)
  • Experience designing and developing production software systems
  • Strong understanding of:
    • Multithreading and concurrency
    • Memory management
    • Performance optimization
    • Software architecture and system design
  • Experience working with simulation, robotics, gaming, or autonomous systems
Preferred Qualifications
  • Experience with simulation platforms such as:
    • CARLA
    • Isaac Sim
    • Unreal Engine
  • Familiarity with reinforcement learning concepts and workflows
  • Experience with agent-based simulation or closed-loop simulation systems
  • Experience building synthetic data generation pipelines
  • Experience with ROS or ROS2
  • Experience with cloud-native infrastructure such as Docker, Kubernetes, AWS, or GCP
  • Familiarity with machine learning infrastructure and large-scale data processing systems
Nice to Have
  • Experience in autonomous driving or robotics applications
  • Experience with multi-agent simulation systems
  • Familiarity with world models, generative simulation, or Physical AI technologies
  • Experience with sensor simulation, including camera, lidar, or radar
  • Experience with physics engines and real-time systems
  • Experience with CUDA, OpenGL, Vulkan, or graphics programming
What We're Looking For
  • Strong software engineering fundamentals
  • Systems-thinking mindset and attention to detail
  • Curiosity about simulation, AI, robotics, and autonomous systems
  • Ability to work across simulation, infrastructure, and machine learning domains
  • Comfortable working in a fast-paced environment with evolving technical challenges
  • Passion for building the next generation of intelligent simulation platforms

Apply