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Reinforcement Learning Engineer Jobs in Austin, TX

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

Reinforcement Learning Engineer

Austin, TX · On-site

As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated ...

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 ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site

Architect and implement reinforcement learning systems for sequential decision-making, including ... Collaborate with engineering teams to integrate AI models into applications and validate ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

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

Reinforcement Learning Engineer information

See Austin, TX salary details

$37.7K

$114.8K

$189.8K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for reinforcement learning engineer in Austin, TX is $114,846.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,300.00 and $150,200.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

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 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 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 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 Austin, TX?

For Reinforcement Learning Engineer jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Austin, TX look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Austin, TX are:

What cities near Austin, TX are hiring for Reinforcement Learning Engineer jobs?

Cities near Austin, TX with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Austin, TX as of August 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $114,846 per year, or $55.2 per hour.

Reinforcement Learning Engineer

Apptronik

Austin, TX

Full-time

Posted 15 days ago

Job description

JOB SUMMARY:

As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated neural network topologies and implementing state-of-the-art RL algorithms to achieve world-class performance in whole-body locomanipulation. You will work alongside a multidisciplinary team to develop high-performance policies and optimized training pipelines that allow our robots to move and interact with the world with unprecedented fluidity. Beyond your technical contributions, you will play a key role in maintaining a high-velocity, ego-free engineering culture - sharing insights, participating in rigorous code reviews, and collaborating closely with hardware and controls teams to ensure our collective success on physical hardware.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES:
  • 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.
  • Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
  • 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.
 SKILLS AND REQUIREMENTS
  • Hands-on expertise (3+ 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.
EDUCATION and/or EXPERIENCE:
  • A PhD degree in Computer Science, Robotics, or a related field, or an MS degree in a similar field with 2+ years industry experience.
  • 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.
PHYSICAL REQUIREMENTS:
  • Prolonged periods of sitting at a desk and working on a computer
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

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