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Reinforcement Learning Jobs in Austin, TX (NOW HIRING)

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

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

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 ...

Apptronik

Software Engineer - Human Motion Data

Austin, TX

$113K - $136K/yr

JOB SUMMARY As a Software Engineer- Human Motion Data, you will leverage your background in robotics to build the crucial link between human-data and our reinforcement learning pipelines. This role ...

Apptronik

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 ...

Apptronik

Software Engineer - Human Motion Data

Austin, TX · On-site

$113K - $136K/yr

As a Software Engineer - Human Motion Data, you will build motion data pipelines and integrate diverse sources to generate accurate human motion trajectories for reinforcement learning applications.

Apptronik

Software Engineer - Human Motion Data

Austin, TX · On-site

$113K - $136K/yr

The Software Engineer - Human Motion Data will build crucial links between human data and reinforcement learning pipelines, architecting robust motion data pipelines and integrating diverse sources ...

Roku

Senior Machine Learning Engineer

Austin, TX

$121K - $160K/yr

We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

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 Austin, TX salary details

$28K

$57.3K

$78.5K

How much do reinforcement learning jobs pay per year?

As of Jul 30, 2026, the average yearly pay for reinforcement learning in Austin, TX is $57,258.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,600.00 and $66,700.00 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.
What are the most commonly searched types of Reinforcement Learning jobs in Austin, TX? The most popular types of Reinforcement Learning jobs in Austin, TX are:
What are popular job titles related to Reinforcement Learning jobs in Austin, TX? For Reinforcement Learning jobs in Austin, TX, the most frequently searched job titles are:
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What cities near Austin, TX are hiring for Reinforcement Learning jobs? Cities near Austin, TX with the most Reinforcement Learning job openings:
Reinforcement Learning jobs near you
Infographic showing various Reinforcement Learning job openings in Austin, TX as of July 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $57,258 per year, or $27.5 per hour.

Senior Reinforcement Learning Engineer

Apptronik

Austin, TX • On-site

$103K - $142K/yr

Full-time

Re-posted 4 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 leverage their expertise in reinforcement learning to solve locomotion and manipulation challenges, mentor junior engineers, and implement advanced learning algorithms for the company's humanoid robots.
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 201-500 employees. The company is currently Growth Stage.

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