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Ai Reinforcement Learning Jobs (NOW HIRING)

poolside

Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

Poolside exists to be this company : to build a world where AI will be the engine behind ... ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ...

Centific

Applied Reinforcement Learning Engineer

About Centific Centific is a frontier AI data foundry that curates diverse, high-quality data ... Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ...

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How much do ai reinforcement learning jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for ai reinforcement learning in the United States is $40.70, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $52.88 per hour, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior AI reinforcement learning engineers with extensive experience, advanced skills in machine learning frameworks, and a strong track record in deploying complex models can earn salaries approaching or exceeding $500,000 annually, especially in high-demand industries like tech and finance. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized expertise and impact on product development.

Will MLE be replaced by AI?

In the context of AI reinforcement learning, machine learning engineers (MLEs) design, develop, and optimize algorithms that enable AI systems to learn from data and interactions. While AI advancements automate certain tasks, MLEs remain essential for creating, tuning, and maintaining reinforcement learning models, especially in complex environments. The role continues to evolve with new tools and frameworks, but it is unlikely to be fully replaced by AI itself in the near future.

What are some common challenges faced by AI Reinforcement Learning specialists when deploying models in real-world applications?

AI Reinforcement Learning (RL) specialists often encounter challenges such as ensuring the reliability and safety of RL agents outside of controlled environments. Real-world data can be noisy and unpredictable, making it difficult for models trained in simulations to generalize. Additionally, RL algorithms typically require significant computational resources and time for training, which can be a constraint in fast-paced projects. Collaboration with domain experts and software engineers is essential to adapt algorithms to production systems and continuously monitor performance for unexpected behaviors.

What are the key skills and qualifications needed to thrive as an AI Reinforcement Learning Specialist, and why are they important?

To thrive as an AI Reinforcement Learning Specialist, you need strong expertise in machine learning, deep learning, and mathematics, usually backed by a degree in computer science, engineering, or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow or PyTorch, and experience with RL-specific libraries like OpenAI Gym are typically required. Analytical thinking, problem-solving abilities, and effective collaboration are essential soft skills for excelling in this role. These skills and qualifications are crucial for developing, optimizing, and deploying RL algorithms that solve complex, real-world problems.

What is the difference between Ai Reinforcement Learning vs Data Scientist?

AspectAi Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of algorithmsDegree in Statistics, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, data analysis teams, consulting firms
Industry UsageAI product development, autonomous systems, roboticsBusiness insights, predictive modeling, data analysis
Common Search/ComparisonYesYes

Ai Reinforcement Learning focuses on developing algorithms that enable machines to learn through trial and error to make decisions. Data Scientists analyze data to extract insights and build predictive models. While both roles require programming skills and a background in data or algorithms, reinforcement learning specialists primarily work on AI systems that learn from interactions, whereas Data Scientists focus on interpreting data to inform business decisions.

Which 3 jobs will survive AI?

Reinforcement learning specialists, data scientists, and AI ethics professionals are likely to continue thriving as AI advances, due to their expertise in developing, managing, and overseeing AI systems. These roles require advanced technical skills, critical thinking, and understanding of complex algorithms, making them less susceptible to automation. Continuous learning and certification in AI tools and frameworks can further enhance job security in these fields.

What is AI reinforcement learning?

AI reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which it uses to improve its future performance. Reinforcement learning is widely used in applications such as robotics, game playing, recommendation systems, and autonomous vehicles. Unlike supervised learning, RL doesn't require labeled input/output pairs and learns through trial and error.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI architect, often requiring advanced skills in reinforcement learning, deep learning, and data analysis. These roles usually involve leadership responsibilities, extensive experience, and may be found in large tech companies or specialized AI firms, with compensation including salary, bonuses, and stock options. Such positions are rare and highly competitive, often requiring advanced degrees and a strong track record of AI project success.
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Ai Reinforcement Learning jobs near you
Infographic showing various Ai Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $84,648 per year, or $40.7 per hour.

Sr. ML Scientist (Pricing & Reinforcement Learning)

Futran Tech Solutions Pvt. Ltd.

Plano, TX • On-site

Full-time

Re-posted 21 days ago

Job description

About Us:
LTIMindtree is a global technology consulting and digital solutions company that enables enterprises across industries to reimagine business models, accelerate innovation, and maximize growth by harnessing digital technologies. As a digital transformation partner to more than 700+ clients, LTIMindtree brings extensive domain and technology expertise to help drive superior competitive differentiation, customer experiences, and business outcomes in a converging world. Powered by nearly 90,000 talented and entrepreneurial professionals across more than 30 countries, LTIMindtree - a Larsen & Toubro Group company - combines the industry-acclaimed strengths of erstwhile Larsen and Toubro Infotech and Mindtree in solving the most complex business challenges and delivering transformation at scale. For more information, please visit .
Job Title:
Senior ML Scientist (Pricing Reinforcement Learning)
Work Location:
Plano TX
Work Mode:
Remote
Job Description:
Role Overview
We seek a Senior ML Scientist to drive innovation in AI MLbased dynamic pricing algorithms and personalized offer experiences This role will focus on designing and implementing advanced machine learning models including reinforcement learning techniques like Contextual Bandits Qlearning SARSA and more By leveraging algorithmic expertise in classical ML and statistical methods you will develop solutions that optimize pricing strategies improve customer value and drive measurable business impact
Key Responsibilities
Algorithm Development Conceptualize design and implement state-of-the-art ML models for dynamic pricing and personalized recommendations
Reinforcement Learning Expertise Develop and apply RL techniques including Contextual Bandits Qlearning SARSA and concepts like Thompson Sampling and Bayesian Optimization to solve pricing and optimization challenges
AI Agents for Pricing Build AIdriven pricing agents that incorporate consumer behaviour demand elasticity and competitive insights to optimize revenue and conversion
Rapid ML Prototyping Experience in quickly building testing and iterating on ML prototypes to validate ideas and refine algorithms
Feature Engineering Engineer largescale consumer behavioural feature stores to support ML models ensuring scalability and performance
CrossFunctional Collaboration Work closely with Marketing Product and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact
Controlled Experiments Design analyze and troubleshoot AB and multivariate tests to validate the effectiveness of your models
Qualifications
8 years in machine learning 5 years in reinforcement learning recommendation systems pricing algorithms pattern recognition or artificial intelligence
Expertise in classical ML techniques eg Classification Clustering Regression using algorithms like XGBoost Random Forest SVM and KMeans with handson experience in RL methods such as Contextual Bandits Qlearning SARSA and Bayesian approaches for pricing optimization
Proficiency in handling tabular data including sparsity cardinality analysis standardization and encoding
Proficient in Python and SQL including Window Functions Group By Joins and Partitioning
Experience with ML frameworks and libraries such as scikitlearn TensorFlow and PyTorch
Knowledge of controlled experimentation techniques including causal AB testing and multivariate testing
Required Skills:
5+ Yrs Expereince in Pricing Reinforcement Learning
8+ Yrs Experience in Machine Learning
Expert in Python & Tabular Data
SQL
Knowledge of AB Testing

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