ZipRecruiter

Log In

1

Reinforcement Learning Jobs in Texas (NOW HIRING)

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

Apple

SoC Machine Learning Design Engineer

Austin, TX · On-site

Experience with Deep neural networks and reinforcement learning is a plus Solid math background and understanding of algorithms and data structures Experience with current deep learning frameworks ...

previous page

Showing results 41-60

All JobsReinforcement Learning JobsReinforcement Learning Jobs in Texas

Reinforcement Learning information

See Texas salary details

$26.6K

$54.4K

$74.5K

How much do reinforcement learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for reinforcement learning in Texas is $54,359.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $63,400.00 per year, depending on experience, location, and employer.

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 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 are the most commonly searched types of Reinforcement Learning jobs in Texas? The most popular types of Reinforcement Learning jobs in Texas are:
What are popular job titles related to Reinforcement Learning jobs in Texas? For Reinforcement Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning jobs in Texas look for? The top searched job categories for Reinforcement Learning jobs in Texas are:
What cities in Texas are hiring for Reinforcement Learning jobs? Cities in Texas with the most Reinforcement Learning job openings:
Reinforcement Learning jobs near you
Infographic showing various Reinforcement Learning job openings in Texas as of August 2026, with employment types broken down into 63% Full Time, 28% Part Time, and 9% Contract. Highlights an 100% In-person job distribution, with an average salary of $54,359 per year, or $26.1 per hour.

Lead Machine Learning Inference Engineer, Advertising

Roku

Austin, TX

$101K - $133K/yr

Full-time

Re-posted 2 days ago

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 and Inference Platform that powers the entire landscape. 

About the role  

In this role, you will architect, design, and lead the development of a SOTA Inference platform that can handle Advertising-level low latencies, scale, throughput, and availability with optimizations that span across hardware, software, and models. We're looking for a strong technical leader with deep experience in ML serving, high-performance computing, and industry standard frameworks - someone excited to mentor engineers, innovate at scale, and shape the future of machine learning at Roku.

What you'll be doing
  • Lead the design and development of a SOTA Inference platform  
  • Oversee the development of monitoring, observability, and other tooling to ensure system and model performance, reliability, and scalability of online inference services
  • Identify and resolve system inefficiencies, performance bottlenecks, and reliability issues, ensuring optimized end-to-end performance 
  • Stay at the forefront of advancements in inference frameworks, ML hardware acceleration,  and distributed systems, and incorporate innovations where and when they are impactful
We're excited if you have
  • M.S. or above in CS, ECE, or a related field 
  • 10+ years of experience in developing and deploying large-scale, distributed systems, with at least 5 years in a leadership or technical lead role  
  • Strong programming skills in high-performance languages
  • Deep understanding of inference frameworks and ML system deployment
  • Proven experience optimizing performance for large-scale machine learning systems, including a deep knowledge of SOTA model optimizations, hardware-software co-design, GPU acceleration, and HPC techniques
  • Excellent communication and collaboration skills
  • Experience leading teams working on high-throughput, low-latency ML serving systems
  • Experience collaborating with and leading global, cross-functional teams
  • Contributions to open-source ML or systems projects
#LI-DH2

Apply