1

Reinforcement Learning Jobs in Austin, TX (NOW HIRING)

AI Engineer - Remote

Austin, TX ยท 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 ...

Backend Developer - Remote

Austin, TX ยท 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 ...

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

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

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

... with Reinforcement Learning (RL), Prompt Engineering, and Knowledge Graphs to improve AI agent capabilities. โ€ข Collaborate with cross-functional teams to integrate AI-powered solutions into ...

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 ...

Senior Machine Learning Engineer

Austin, TX ยท 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

Showing results 21-40

Reinforcement Learning information

See Austin, TX salary details

$28.2K

$57.8K

$79.3K

How much do reinforcement learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for reinforcement learning in Austin, TX is $57,820.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $67,400.00 per year, depending on experience, location, and employer.

What is a reinforcement learning?

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.

What does a reinforcement learning professional do?

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?

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 can you do with reinforcement learning?

Reinforcement learning is used in roles such as reinforcement learning engineer or researcher to develop algorithms that enable systems to learn optimal actions through trial and error. It is applied in areas like robotics, game playing, autonomous vehicles, and recommendation systems, often requiring skills in programming, data analysis, and understanding of machine learning frameworks. Professionals in this field design, train, and evaluate models to improve decision-making processes in complex environments.

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:

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

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

Infographic showing various Reinforcement Learning job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $57,834 per year, or $27.8 per hour.

AI Engineer - Remote

YO AI Labs

Austin, TX โ€ข Remote

$80 - $120/hr

Full-time

Posted 8 days ago


Job description

Senior Software Engineer

Job Type: Contractor (~15 hours/week)
Location: Remote

Job Summary

We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.

You will design reproducible environments, deterministic verification, and reference solutions for tasks such as bug fixing, feature implementation, codebase refactoring, and performance optimization. No prior AI experience is required.

Key Responsibilities
  • Create reinforcement learning environments for software engineering tasks.
  • Design tasks involving bug fixing, feature development, refactoring, and performance optimization.
  • Build deterministic verification systems and golden reference solutions.
  • Evaluate AI agents' ability to reason through complex codebases and use MCP tools effectively.
  • Develop realistic, reproducible software engineering scenarios.
  • Ensure tasks accurately measure coding ability, problem-solving, and tool usage.
  • Document solutions and provide clear technical feedback.
Required Skills
  • Strong proficiency in Python 3, Java, Rust, C++, or TypeScript.
  • Strong understanding of algorithms and data structures.
  • Experience with bug fixing and debugging complex software issues.
  • Proven experience in feature implementation and codebase refactoring.
  • Strong knowledge of performance optimization and tuning.
  • Excellent written and verbal communication.
  • Strong attention to detail.
Preferred Qualifications
  • Experience working with large-scale or distributed codebases.
  • Familiarity with AI/ML systems is a plus but not required.
  • Experience with rigorous code reviews and software engineering best practices.
  • Experience working effectively in remote or cross-functional teams.
Hiring Process
  1. Submit an application and screening questions.
  2. Complete an AI interview (~30 minutes).
  3. Complete a technical assessment, if required.
  4. Hiring Manager review.
Compensation

Compensation is output-based, with payment provided per task that meets project specifications. Minimum weekly submission requirements may apply.

Availability

Selected experts should be prepared to begin their first tasks within 24–48 hours of completing onboarding.