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Reinforcement Learning Engineer

Bright Vision Technologies•United States•🌍 Remote
Full-time7-15
$96k - $120k
per year
👁️ 0 views•📝 0 applications•Posted 8/21/2026•Expires 10/20/2026
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Job Description

Reinforcement Learning Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: Reinforcement Learning Engineer Location: 100% Remote (U. S.)

Position Type: Full-time, Direct W2 Salary Range: $96,000–$120,000 Annually Experience Required: 6+ years Sponsorship: U. S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are looking for a Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient.

The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale.

The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical.

Required Qualifications 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 communicatio

Required Skills

Pythondeep learning frameworksreinforcement learningRL libraryreward modelingprobabilityoptimizationneural network policiesGPU clusterssimulation environmentslarge-scale experience collection

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