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We aim to help these organizations unlock significant business value by deploying GenAI at scale ... Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ...

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As of Jun 24, 2026, the average hourly pay for helper reinforcement learning in the United States is $16.44, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $17.55 per hour, depending on experience, location, and employer.

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

AspectHelper Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of reinforcement learningDegree in Data Science, Statistics, Computer Science; proficiency in programming and analytics
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, consulting firms, tech companies
Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights
Common Search/ComparisonHelper Reinforcement Learning vs Data Scientist

Helper Reinforcement Learning focuses on developing algorithms that enable machines to learn through interactions, often requiring knowledge of reinforcement learning techniques. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve programming and data handling, Helper Reinforcement Learning is more specialized in AI algorithm development, whereas Data Scientists work broadly across data analysis and modeling in various industries.

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Helper Reinforcement Learning jobs near you

Reinforcement Learning Engineer

Bright Vision Technologies

Remote

Full-time

Posted 9 days ago

Job description

Job Summary:
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. They are looking for a skilled Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems, requiring familiarity with modern reinforcement learning algorithms and engineering complexity.
Responsibilities:
• Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments
• Develop, calibrate, and maintain simulation environments suitable for large-scale agent training
• Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods
• Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints
• Apply offline RL and imitation learning techniques where exploration is costly or unsafe
• Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant
• Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems
• Optimize training stability and sample efficiency through algorithmic and engineering improvements
• Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases
• Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight
• Collaborate with applied scientists and product teams to identify high-value RL use cases
• Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users
• Document methodology, design decisions, and operational characteristics for internal stakeholders
• Stay current with RL research and translate promising techniques into production-ready solutions
Qualifications:
Required:
• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience
• Six or more years of combined RL research and engineering experience
• Strong proficiency in Python and modern deep learning frameworks
• Hands-on experience with at least one major RL library or in-house RL stack
• Solid understanding of probability, optimization, and the theoretical foundations of RL
• Experience designing and tuning reward functions in non-trivial environments
• Familiarity with simulation environments and large-scale experience collection
• Experience training neural network policies on GPU clusters
• Strong written and verbal communication skills
• Track record of shipping or publishing impactful RL work
Preferred:
• Experience with RLHF for large language models
• Familiarity with multi-agent RL or hierarchical RL
• Exposure to robotics, control systems, or autonomous driving
• Publications in RL or related research venues
• Open-source contributions to RL libraries or environments
Company:
Bright Vision Technologies is an information technology company that offers software development, AI, and cybersecurity services. Founded in 2020, the company is headquartered in Bridgewater, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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