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Deep Reinforcement Learning Jobs in Texas (NOW HIRING)

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site

This role combines deep expertise in both computer vision and large language models with hands-on experience in reinforcement learning to create intelligent systems that can understand, reason about ...

Beth Page tech

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Deep Neural Networks (DNN): * Hands-on experience with CNN, RNN, Graph Neural Networks, and ... Experience with Reinforcement Learning, RAG (Retrieval-Augmented Generation), and Agentic AI.

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

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 ...

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Deep Reinforcement Learning information

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$26.6K

$54.4K

$74.5K

How much do deep reinforcement learning jobs pay per year?

As of Aug 14, 2026, the average yearly pay for deep 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 typical day look like for someone working in deep reinforcement learning?

A typical day for a Deep Reinforcement Learning professional involves designing algorithms, running experiments, analyzing results, and optimizing models to improve performance. You may collaborate regularly with data scientists, software engineers, and domain experts to integrate RL solutions into larger systems or products. Tasks often include reading the latest research, contributing to code reviews, and documenting findings while troubleshooting technical challenges. This dynamic environment encourages continuous learning and teamwork, ensuring you stay at the forefront of AI innovation.

What is deep reinforcement learning?

A Deep Reinforcement Learning (DRL) job involves researching, developing, and applying AI models that use reinforcement learning techniques combined with deep learning. Professionals in this role design algorithms that enable agents to learn optimal decision-making policies through trial and error. Common applications include robotics, game AI, autonomous systems, and financial modeling. This job typically requires expertise in machine learning, neural networks, and programming languages like Python, along with frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in deep reinforcement learning?

To thrive in Deep Reinforcement Learning, you need expertise in machine learning, programming (Python, TensorFlow, or PyTorch), and applied mathematics, often supported by an advanced degree in computer science or a related field. Familiarity with version control systems, cloud computing platforms, and relevant certifications in AI or data science are valuable assets. Strong problem-solving abilities, collaboration, and effective communication are important soft skills in this position. These skills are essential for developing, implementing, and iterating cutting-edge algorithms that solve complex real-world problems in dynamic environments.

What are the most commonly searched types of Deep Reinforcement Learning jobs in Texas?

The most popular types of Deep Reinforcement Learning jobs in Texas are:

What are popular job titles related to Deep Reinforcement Learning jobs in Texas?

For Deep Reinforcement Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Deep Reinforcement Learning jobs in Texas look for?

The top searched job categories for Deep Reinforcement Learning jobs in Texas are:

Deep Reinforcement Learning jobs near you
Infographic showing various Deep Reinforcement Learning job openings in Texas as of August 2026, with employment types broken down into 60% Full Time, and 40% Contract. Highlights an 100% In-person job distribution, with an average salary of $54,359 per year, or $26.1 per hour.

Staff R&D AI Engineer

webAI Inc

Austin, TX • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 23 days ago

Job description

About Us:
We are establishing the first distributed Al infrastructure dedicated to personalized Al. The evolving needs of a data-driven society are demanding scalability and flexibility. We believe that the future of Al is distributed and enables real-time data processing at the edge, closer to where data is generated. We are building a future where a company's data and IP remains private and it's possible to bring large models directly to consumer hardware without removing information from the model.
Role Overview:
As a Staff R&D AI Engineer, you will lead the development of cutting-edge AI systems that bridge computer vision, natural language understanding, and action learning. You'll architect and implement Vision-Language-Action (VLA) models, advance reinforcement learning applications, and push the boundaries of multimodal AI integration. This role combines deep expertise in both computer vision and large language models with hands-on experience in reinforcement learning to create intelligent systems that can understand, reason about, and interact with complex environments. You'll drive research initiatives, mentor technical teams, and translate breakthrough AI research into practical applications across diverse domains.
Key Responsibilities:
  • Design and develop Vision-Language-Action (VLA) models that integrate visual perception, natural language understanding, and action prediction
  • Architect and implement reinforcement learning systems for sequential decision-making, including policy learning and skill acquisition
  • Build and optimize computer vision pipelines for perception tasks, including object detection, segmentation, tracking, and scene understanding
  • Develop and fine-tune large language models for instruction following, reasoning, and task planning applications
  • Implement RLHF (Reinforcement Learning from Human Feedback) systems to improve model alignment and safety
  • Create multimodal training pipelines that leverage synthetic and real-world data for robust model performance
  • Research and prototype novel AI architectures that combine vision, language, and action learning
  • Collaborate with engineering teams to integrate AI models into applications and validate performance across domains
  • Optimize model inference performance for real-time applications across edge and cloud deployments
  • Lead technical initiatives, mentor junior AI engineers, and establish best practices for AI model development
  • Stay current with latest research in VLA models, multimodal AI, and robotics to drive innovation roadmap
  • Present findings at conferences and publish research to advance the field

Qualifications & Skills:
  • 7+ years of experience in AI/ML engineering with 4+ years focusing on deep learning and neural network development
  • Strong understanding of reinforcement learning algorithms and their applications (PPO, SAC, TD3, etc.)
  • Strong expertise in both computer vision and natural language processing with hands-on model development experience
  • Proficiency in PyTorch and/or TensorFlow with experience training and deploying large-scale models
  • Experience with transformer architectures, attention mechanisms, and large language model fine-tuning
  • Hands-on experience with computer vision tasks including object detection, semantic segmentation, and visual tracking
  • Strong programming skills in Python with experience in distributed training and model optimization
  • Understanding of sequential decision-making and control systems fundamentals
  • Experience with MLOps practices including model versioning, monitoring, and deployment pipelines
  • Proven ability to work independently on complex research problems and deliver practical solutions
  • Strong communication skills and experience collaborating with cross-functional engineering teams

Preferred Qualifications:
  • PhD in Computer Science, Robotics, AI/ML, or related field with focus on multimodal learning or robotics
  • Direct experience developing or working with Vision-Language-Action (VLA) models or similar multimodal architectures
  • Experience with RLHF implementation and human feedback integration for model alignment
  • Background in imitation learning, inverse reinforcement learning, or learning from demonstrations
  • Experience with real-world system deployment and sim-to-real transfer techniques
  • Knowledge of 3D computer vision, spatial reasoning, or multi-modal perception systems
  • Experience with distributed training frameworks (DeepSpeed, FairScale, Horovod) and large-scale model training
  • Familiarity with edge AI deployment and model optimization techniques (quantization, pruning, distillation)
  • Experience with embodied AI research or projects involving agent-environment interaction
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR, CoRL, etc.)
  • Open-source contributions to major AI/ML frameworks or robotics projects
  • Startup experience with ability to rapidly prototype and iterate on AI solutions
  • Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies
  • Background in safety-critical AI systems or AI alignment research

We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following:
  • Truth - Emphasizing transparency and honesty in every interaction and decision.
  • Ownership - Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients.
  • Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement.
  • Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others.

Benefits:
We strive to provide competitive benefits to all employees. The benefits listed in this posting generally apply to U.S.-based employees. For employees hired outside the United States, benefits may vary based on local law, country-specific requirements, and the employment platform or entity through which the employee is hired.
  • Competitive salary
  • Comprehensive health, dental, and vision benefits package
  • 401(k) match
  • Equity options
  • $200/month Health & Wellness stipend
  • Continuing Education support
  • $500/year Function Health subscription
  • Free parking for in-office employees
  • Flexible Time Off (FTO)
  • Parental leave for eligible employees
  • Supplemental life insurance

webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

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