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Senior Reinforcement Learning Jobs (NOW HIRING)

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 ...

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Senior Reinforcement Learning information

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$25K

$80.3K

$163.5K

How much do senior reinforcement learning jobs pay per year?

As of Jun 12, 2026, the average yearly pay for senior reinforcement learning in the United States is $80,287.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,500.00 and $103,000.00 per year, depending on experience, location, and employer.

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

Senior Reinforcement Learning professionals often encounter challenges such as ensuring model robustness when transferring algorithms from simulated to real-world environments, handling limited or noisy data, and managing the computational demands of training complex models. Additionally, safety and interpretability are critical, as real-world deployments can have significant impacts if models behave unpredictably. Close collaboration with domain experts and engineering teams is essential to address these challenges and ensure successful, scalable deployments.

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

To thrive as a Senior Reinforcement Learning Engineer, you need deep expertise in machine learning, reinforcement learning algorithms, and programming languages such as Python, often supported by an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries, as well as experience with high-performance computing and cloud platforms, is typically required. Strong problem-solving abilities, collaboration, and communication skills help distinguish top performers in this role. These skills ensure the development of efficient, robust RL models and effective teamwork on complex AI projects.

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

AspectSenior Reinforcement LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; experience with RL frameworksDegree in CS, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, AI teams, tech companies focusing on ML projectsBusiness analytics, data analysis, and modeling in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, marketing, tech, and more

While both roles require strong analytical skills and technical knowledge, Senior Reinforcement Learning specialists focus on developing RL algorithms and models, often in AI research settings. Data Scientists analyze data to inform business decisions across industries. The roles overlap in data handling and programming but differ in their core focus and application areas.

What does a Senior Reinforcement Learning Engineer do?

A Senior Reinforcement Learning Engineer designs, develops, and implements advanced machine learning algorithms that enable systems to learn optimal behaviors through trial and error. They work on complex problems such as robotics, game AI, recommendation systems, and automated decision-making. In addition to coding and model development, they often lead research initiatives, collaborate with cross-functional teams, and mentor junior engineers. Their role requires deep knowledge of reinforcement learning theory, practical experience with machine learning frameworks, and strong programming skills.
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Senior Reinforcement Learning jobs near you
Infographic showing various Senior Reinforcement Learning job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.
Senior Reinforcement Learning Engineer

Senior Reinforcement Learning Engineer

Apptronik

Austin, TX • On-site

$103K - $142K/yr

Full-time

Posted 16 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.

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