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Reinforcement Learning Engineer Jobs in Houston, TX

Beth Page tech

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Senior Machine Learning Engineer Location: Houston, TX Environment: Standard, 5-days onsite : Must ... Experience with Reinforcement Learning, RAG (Retrieval-Augmented Generation), and Agentic AI.

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks ... Establish prompt engineering and data management best practices for transparency and governance.

NobleAI

Lead Machine Learning Engineer

Houston, TX · Remote

$104K - $138K/yr

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks ... Establish prompt engineering and data management best practices for transparency and governance.

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks ... Establish prompt engineering and data management best practices for transparency and governance.

Scalence L.L.C.

AI ML Software Engineer

Houston, TX · On-site

... learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs. • Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune ...

Persona AI

Autonomy Engineer

Houston, TX · On-site

$97K - $128K/yr

Autonomy Engineer Department: Software Reports To: Behavior Coordination Lead Employment Type ... Experience with NVIDIA Isaac Lab for reinforcement learning environment setup and training

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Houston, TX

Reinforcement Learning Engineer information

See Houston, TX salary details

$36.3K

$110.6K

$182.9K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 1, 2026, the average yearly pay for reinforcement learning engineer in Houston, TX is $110,647.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,300.00 and $144,700.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

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

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Houston, TX? For Reinforcement Learning Engineer jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Houston, TX look for? The top searched job categories for Reinforcement Learning Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Reinforcement Learning Engineer jobs? Cities near Houston, TX with the most Reinforcement Learning Engineer job openings:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Houston, TX as of July 2026, with employment types broken down into 72% Full Time, and 28% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $110,647 per year, or $53.2 per hour.

Reinforcement Learning Engineer, Grasping

Persona AI

Houston, TX • On-site

Full-time

Re-posted 14 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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