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Reinforcement Learning Engineer Jobs in Austin, TX

Roku

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction ... We're looking for seasoned engineers with a background in machine learning to aid in this mission.

YO AI Labs

Backend Engineer - Remote

Austin, TX · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

YO AI Labs

AI Engineer - Remote

Austin, TX · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

YO AI Labs

AI Software Engineer - Remote

Austin, TX · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

YO AI Labs

RL Environment Engineer - Remote

Austin, TX · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

YO AI Labs

AI/ML Software Engineer - Remote

Austin, TX · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

Adidev Technologies Inc

ML/AI Engineers

Austin, TX · On-site

$120 - $180/hr

Summary Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying ... Demonstrable experience in machine learning, deep learning, NLP, computer vision, reinforcement ...

Adidev Technologies Inc

ML/AI Engineers

Austin, TX · On-site

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... reinforcement learning, and/or other AI domains. · Demonstrable experience with Generative AI ...

Adidev Technologies Inc

ML/AI Engineers

Austin, TX · On-site

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... vision, reinforcement learning, and/or other AI domains. • Demonstrable experience with ...

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Showing results 21-40

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 Sep 5, 2026, the average yearly pay for reinforcement learning engineer in Austin, TX is $114,845.00, 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 87% Full Time, and 13% Contract. Highlights an 79% In-person, and 21% Remote job distribution, with an average salary of $114,845 per year, or $55.2 per hour.

Senior Machine Learning Engineer

Roku

Austin, TX • On-site

$121K - $160K/yr

Full-time

Re-posted 21 days ago

Key responsibilities

  • Build and maintain a machine learning platform that manages the entire model lifecycle, including feature engineering, training, versioning, deployment, and monitoring.

  • Apply expertise in data analysis and feature engineering to generate features for multiple use cases and models.

  • Develop and evaluate machine learning models using techniques such as Decision Trees, Logistic Regression, Neural Networks, and Bayesian Analysis for improving system performance and accuracy.

Job description

About the team 

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. 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, Experimentation, and Inference Platform that powers the entire landscape, which we continuously evolve over time.

About the role 

We're on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly-profitable campaigns. The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience. We're looking for seasoned engineers with a background in machine learning to aid in this mission. Examples of problems include improving ad relevance, inferring demographics, yield optimization, and many more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning using both general purpose software and statistical languages.

What you'll be doing 
  • ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality
  • Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
  • Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy
  • Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters
  • Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions
  • Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends
We're excited if you have 
  • Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field
  • 5 years of experience in applied machine learning on real use cases 
  • Proficient coding skills and strong software development experience in Spark, Python, or Java
  • Familiarity with real-time evaluation of models with low latency constraints
  • Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
  • Ability to work with large scale computing frameworks, data analysis systems, and modelling environments i.e. Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB
  • Ad Tech experience is preferred 
  • Proficient use of AI tools and agentic coding practices 
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