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Reinforcement Learning Jobs in Texas (NOW HIRING)

You will collaborate closely with Perception, Navigation, Controls, Reinforcement Learning, and Platform teams, and help shape Apptronik's long-term autonomy strategy. Design and implement mission ...

AI Developer

Mckinney, TX · On-site

$100K - $130K/yr

Neural Networks, Decision Trees, SVM, NLP, Reinforcement Learning, Ensemble Methods, MCP • Strong knowledge with RAG (Retrieval-Augmented Generation), Prompt Engineering, Agentic AI • Knowledge ...

Software Engineer - Human Motion Data

Austin, TX · On-site

$113K - $136K/yr

Collaborate closely with the Reinforcement Learning and Controls teams to iterate on data requirements, understand failure modes, and ensure the generated trajectories are physically viable on ...

Software Engineer - Human Motion Data

Austin, TX · On-site

$113K - $136K/yr

Collaborate closely with the Reinforcement Learning and Controls teams to iterate on data requirements, understand failure modes, and ensure the generated trajectories are physically viable on ...

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus.Experience with Spark, TensorFlow, Keras, and PyTorch a plus

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Reinforcement Learning information

See Texas salary details

$26.6K

$54.4K

$74.5K

How much do reinforcement learning jobs pay per year?

As of Jun 16, 2026, the average yearly pay for reinforcement learning in Texas is $54,359.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $63,400.00 per year, depending on experience, location, and employer.

What are the common responsibilities of a Reinforcement Learning professional on a daily basis?

A typical day for a Reinforcement Learning professional involves designing and implementing learning algorithms, running experiments, analyzing data, and iterating on models to improve performance. You might collaborate closely with data scientists, software engineers, and product managers to integrate your solutions into broader systems or products. Regular activities also include reading recent research literature and participating in team meetings to discuss progress and obstacles. This dynamic role often balances deep technical work with teamwork to drive innovative applications in areas such as robotics, recommendation systems, or autonomous systems.

Who earns more, AI or ML engineer?

Reinforcement Learning engineers, a specialized subset of AI and ML engineers, tend to earn higher salaries due to their advanced skills in developing algorithms for decision-making systems. Overall, AI engineers generally have higher average salaries than ML engineers, but salaries vary based on experience, location, and industry. Both roles require strong programming skills and knowledge of machine learning frameworks.

What are the key skills and qualifications needed to thrive in the Reinforcement Learning position, and why are they important?

To thrive in a Reinforcement Learning role, you need a solid background in mathematics, statistics, machine learning, and programming (commonly with Python), typically supported by a relevant degree such as in computer science or engineering. Experience with frameworks like TensorFlow, PyTorch, OpenAI Gym, and familiarity with large-scale computing systems are highly valued. Strong problem-solving abilities, curiosity, and effective collaboration and communication skills help you excel in multidisciplinary research and project teams. These capabilities are crucial for designing, implementing, and refining complex algorithms that learn from interaction to solve real-world problems.

What engineers make $500,000?

Senior reinforcement learning engineers with extensive experience, advanced skills in machine learning frameworks, and a strong track record in deploying AI systems can earn salaries approaching or exceeding $500,000, especially in high-cost-of-living areas or within leading tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized expertise and impact on product development.

Which 5 jobs will survive AI?

Reinforcement Learning specialists, data scientists, AI researchers, software engineers, and cybersecurity analysts are likely to continue thriving as AI advances, due to their expertise in developing, managing, and securing AI systems. These roles require advanced technical skills, problem-solving abilities, and ongoing learning to adapt to evolving technologies.

What is a Reinforcement Learning job?

A Reinforcement Learning (RL) job involves designing, developing, and optimizing algorithms that enable machines to learn from interactions with their environment. RL professionals work on applications in robotics, finance, gaming, and autonomous systems, leveraging techniques like deep reinforcement learning and policy optimization. Responsibilities often include researching new models, implementing RL algorithms, and improving AI performance. Strong programming skills, knowledge of machine learning frameworks, and an understanding of mathematical concepts like probability and optimization are essential.

Which 3 jobs will survive AI?

Reinforcement Learning specialists, data scientists, and AI ethics professionals are likely to remain in demand as AI advances, due to their specialized skills in developing, managing, and overseeing AI systems. These roles require advanced knowledge of algorithms, programming, and ethical considerations, making them less susceptible to automation. Continuous learning and expertise in AI tools and frameworks help ensure job security in this evolving field.
What are the most commonly searched types of Reinforcement Learning jobs in Texas? The most popular types of Reinforcement Learning jobs in Texas are:
What job categories do people searching Reinforcement Learning jobs in Texas look for? The top searched job categories for Reinforcement Learning jobs in Texas are:
What cities in Texas are hiring for Reinforcement Learning jobs? Cities in Texas with the most Reinforcement Learning job openings:
Infographic showing various Reinforcement Learning job openings in Texas as of June 2026, with employment types broken down into 42% Full Time, 25% Part Time, and 33% Contract. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $54,359 per year, or $26.1 per hour.
Senior Autonomy Software Engineer

Senior Autonomy Software Engineer

Apptronik

Austin, TX

$190K - $235K/yr

Other

Posted 4 days ago


Job description

Senior Autonomy Software Engineer

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.

As a Senior Software Engineer on the Autonomy team at Apptronik, you will design and deploy learning-driven, mission-level autonomy systems that enable humanoid robots to operate robustly in real-world human environments. Your work focuses on the coordination, execution, and adaptation of robot behaviors using learning-based approaches rather than hand-authored task planners.

You will build the software that allows humanoid robots to reason over goals, adapt to dynamic environments, and execute complex missions by integrating outputs from perception, navigation, manipulation, and control systems. This role sits at the intersection of autonomy research and production engineering, with a strong emphasis on real-world deployment, robustness, and scalability.

You will collaborate closely with Perception, Navigation, Controls, Reinforcement Learning, and Platform teams, and help shape Apptronik's long-term autonomy strategy.

Design and implement mission-level autonomy systems for humanoid robots, focusing on learning-based decision making and behavior execution.

Develop policy execution, monitoring, and coordination layers that integrate learning-based components with classical robot subsystems.

Build autonomy frameworks that support adaptive behavior, generalization across tasks, and robustness to uncertainty and environmental variation.

Implement recovery, fallback, and safety mechanisms around learning-based autonomy to ensure reliable real-world operation.

Define and maintain clean interfaces between autonomy, perception, navigation, manipulation, and control systems.

Collaborate with Reinforcement Learning teams to integrate trained policies into real-time robot software stacks.

Develop infrastructure for telemetry, logging, evaluation, and replay to understand and debug autonomy behavior.

Validate autonomy systems in simulation and on physical humanoid robots, closing the loop from research to deployment.

Contribute to autonomy architecture, code quality, CI/CD pipelines, and long-term maintainability.

Mentor junior engineers and provide technical leadership within the autonomy organization.

Strong proficiency in modern C++ and working knowledge of Python in Linux environments.

Experience integrating learning-based policies (e.g., reinforcement learning, imitation learning, foundation-model-based policies) into real robot systems.

Solid understanding of robotics systems, including: state representation and estimation interfaces, interaction between autonomy, perception, navigation, and control, real-time and distributed software systems.

Experience deploying autonomy software on physical robots, including debugging and tuning under real-world constraints.

Familiarity with ROS 2, message-passing architectures, and modular robot software design.

Strong software engineering fundamentals: testing, CI/CD, code reviews, documentation, and system reliability.

Experience with humanoid robots, mobile manipulators, or legged robotic systems.

Hands-on experience with reinforcement learning or learning-based control for robotics.

Experience designing safe wrappers, monitors, or supervisors around learning-based systems.

Contributions to open-source robotics, autonomy, or ML infrastructure.

Experience working in fast-paced robotics startups or deploying systems into production.

MS, or PhD in Robotics, Computer Science, Computer Engineering, or a related field.

2+ years of experience developing robot autonomy or learning-based robotic systems.

Prolonged periods of sitting at a desk and working on a computer

Must be able to lift 15 pounds at times

Vision to read printed materials and a computer screen

Hearing and speech to communicate

The annual salary range is $190,000 - $235,000

*This is a direct hire. Please, no outside Agency solicitations.

Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.