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Reinforcement Learning Jobs in Texas (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will focus on achieving state-of-the-art performance on humanoid robots, leveraging expertise in reinforcement learning to solve locomotion and manipulation ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will leverage their expertise in reinforcement learning to solve locomotion and manipulation challenges, mentor junior engineers, and implement advanced ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will focus on achieving state-of-the-art performance on humanoid robots by implementing and deploying advanced learning algorithms while mentoring junior ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site

Architect and implement reinforcement learning systems for sequential decision-making, including policy learning and skill acquisition * Build and optimize computer vision pipelines for perception ...

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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 Jul 14, 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.

Will MLE be replaced by AI?

In reinforcement learning, machine learning engineers (MLEs) design, implement, and optimize algorithms that enable AI systems to learn from interactions. While AI continues to advance, MLEs play a crucial role in developing and fine-tuning models, and their skills remain essential for deploying effective reinforcement learning solutions. The role is evolving with increased automation, but MLEs are unlikely to be fully replaced in the near term.

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.

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 around $500,000 or higher, especially in top tech companies or specialized research roles. Compensation often includes base salary, bonuses, and stock options, reflecting expertise in AI, deep learning, and programming languages like Python or C++.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior reinforcement learning engineer or research scientist, often requiring advanced skills in machine learning, deep learning, and programming. These roles usually involve leading projects, developing innovative algorithms, and may require extensive experience and specialized certifications. Compensation at this level reflects the expertise and impact expected in cutting-edge AI development environments.

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 are essential for long-term job security in these fields.
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Reinforcement Learning jobs near you

Reinforcement Learning Engineer, Grasping

Persona AI

Houston, TX • On-site

Full-time

Posted 26 days ago

Job description

Job Summary:
Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. They are seeking a Reinforcement Learning Engineer to join their Manipulation team, focusing on developing reliable grasping policies for high-DOF robotic hands.
Responsibilities:
• Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.
• Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.
• Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.
• Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.
• Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.
• Collaborate with the rest of the software team to deploy end-to-end grasping systems.
• Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.
• Integrate tactile sensing and feedback into grasp policies for robust, force-aware manipulation.
Qualifications:
Required:
• BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.
• 2+ years of hands-on experience in reinforcement learning for robotic manipulation; exceptional recent graduates from relevant research labs will be considered.
• Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
• Hands-on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
• Experience with sim-to-real transfer: domain randomization, physics tuning, or real-world policy validation on hardware.
• Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
• Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.
Preferred:
• Experience deploying RL-trained policies on physical robotic hands.
• Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
• Experience with contact-rich manipulation and force/torque estimation.
• Familiarity with other learning-based approaches such as behavior cloning, imitation learning, or diffusion-based policy methods.
• Publications or project work at top-tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
• Experience in a humanoid robot startup environment.
Company:
Persona AI is a robotics company that provides robotic solutions. Founded in 2024, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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