ZipRecruiter

Log In

1

Ai Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will focus on achieving ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will focus on achieving ...

OVA.Work

Applied AI Scientist

Preferred : • Experience with multimodal AI, reinforcement learning, or agentic AI systems. • Familiarity with distributed training and large-scale model deployment. • Experience with cloud AI ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

We operate at the cutting edge of embodied AI, applying our expertise across the full robotics ... JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ...

next page

Showing results 1-20

All JobsAi Reinforcement Learning Jobs

Ai Reinforcement Learning information

See salary details

$26

$40

$69

How much do ai reinforcement learning jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for ai reinforcement learning in the United States is $40.70, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $52.88 per hour, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior AI reinforcement learning engineers with extensive experience, advanced skills in machine learning frameworks, and a strong track record in deploying complex models can earn salaries approaching or exceeding $500,000 annually, especially in high-demand industries like tech and finance. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized expertise and impact on product development.

Will MLE be replaced by AI?

In the context of AI reinforcement learning, machine learning engineers (MLEs) design, develop, and optimize algorithms that enable AI systems to learn from data and interactions. While AI advancements automate certain tasks, MLEs remain essential for creating, tuning, and maintaining reinforcement learning models, especially in complex environments. The role continues to evolve with new tools and frameworks, but it is unlikely to be fully replaced by AI itself in the near future.

What are some common challenges faced by AI Reinforcement Learning specialists when deploying models in real-world applications?

AI Reinforcement Learning (RL) specialists often encounter challenges such as ensuring the reliability and safety of RL agents outside of controlled environments. Real-world data can be noisy and unpredictable, making it difficult for models trained in simulations to generalize. Additionally, RL algorithms typically require significant computational resources and time for training, which can be a constraint in fast-paced projects. Collaboration with domain experts and software engineers is essential to adapt algorithms to production systems and continuously monitor performance for unexpected behaviors.

What are the key skills and qualifications needed to thrive as an AI Reinforcement Learning Specialist, and why are they important?

To thrive as an AI Reinforcement Learning Specialist, you need strong expertise in machine learning, deep learning, and mathematics, usually backed by a degree in computer science, engineering, or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow or PyTorch, and experience with RL-specific libraries like OpenAI Gym are typically required. Analytical thinking, problem-solving abilities, and effective collaboration are essential soft skills for excelling in this role. These skills and qualifications are crucial for developing, optimizing, and deploying RL algorithms that solve complex, real-world problems.

What is the difference between Ai Reinforcement Learning vs Data Scientist?

AspectAi Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of algorithmsDegree in Statistics, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, data analysis teams, consulting firms
Industry UsageAI product development, autonomous systems, roboticsBusiness insights, predictive modeling, data analysis
Common Search/ComparisonYesYes

Ai Reinforcement Learning focuses on developing algorithms that enable machines to learn through trial and error to make decisions. Data Scientists analyze data to extract insights and build predictive models. While both roles require programming skills and a background in data or algorithms, reinforcement learning specialists primarily work on AI systems that learn from interactions, whereas Data Scientists focus on interpreting data to inform business decisions.

Which 3 jobs will survive AI?

Reinforcement learning specialists, data scientists, and AI ethics professionals are likely to continue thriving as AI advances, due to their expertise in developing, managing, and overseeing AI systems. These roles require advanced technical skills, critical thinking, and understanding of complex algorithms, making them less susceptible to automation. Continuous learning and certification in AI tools and frameworks can further enhance job security in these fields.

What is AI reinforcement learning?

AI reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which it uses to improve its future performance. Reinforcement learning is widely used in applications such as robotics, game playing, recommendation systems, and autonomous vehicles. Unlike supervised learning, RL doesn't require labeled input/output pairs and learns through trial and error.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI architect, often requiring advanced skills in reinforcement learning, deep learning, and data analysis. These roles usually involve leadership responsibilities, extensive experience, and may be found in large tech companies or specialized AI firms, with compensation including salary, bonuses, and stock options. Such positions are rare and highly competitive, often requiring advanced degrees and a strong track record of AI project success.
More about Ai Reinforcement Learning jobs
What cities are hiring for Ai Reinforcement Learning jobs? Cities with the most Ai Reinforcement Learning job openings:
What states have the most Ai Reinforcement Learning jobs? States with the most job openings for Ai Reinforcement Learning jobs include:
What job categories do people searching Ai Reinforcement Learning jobs look for? The top searched job categories for Ai Reinforcement Learning jobs are:
Ai Reinforcement Learning jobs near you
Infographic showing various Ai Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $84,648 per year, or $40.7 per hour.
Senior Reinforcement Learning Engineer

Senior Reinforcement Learning Engineer

Apptronik

Austin, TX • On-site

$103K - $142K/yr

Full-time

Re-posted 11 days ago

Job description

Job Summary:
Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. 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 challenges and mentor junior engineers.
Responsibilities:
• Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
• Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
• Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
Qualifications:
Required:
• Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
• Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
• A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.
• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.
Company:
Apptronik is a robotics company that designs and builds humanoid robots for various real-world applications. Founded in 2016, the company is headquartered in Austin, USA, with a team of 51-200 employees. The company is currently Growth Stage.

Apply